CN115457777A - Specific vehicle traceability analysis method - Google Patents
Specific vehicle traceability analysis method Download PDFInfo
- Publication number
- CN115457777A CN115457777A CN202211081346.XA CN202211081346A CN115457777A CN 115457777 A CN115457777 A CN 115457777A CN 202211081346 A CN202211081346 A CN 202211081346A CN 115457777 A CN115457777 A CN 115457777A
- Authority
- CN
- China
- Prior art keywords
- data
- target vehicle
- vehicle
- gps
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
-
- 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
-
- 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
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a specific vehicle traceability analysis method, which comprises the following steps: acquiring a vehicle scattering position and scattering time, judging whether the scattering position contains bayonet snapshot data or not based on a preset road traffic police bayonet monitoring system, and acquiring bayonet snapshot data corresponding to the muck vehicle; carrying out characteristic analysis according to the bayonet snapshot data corresponding to the vehicle to determine the characteristics of the target vehicle; and determining the source position of the target vehicle based on a preset muck vehicle capturing base and by combining capturing data of the target vehicle and GPS data according to the characteristics of the target vehicle. The invention can realize the monitoring and analysis of the whole operation flow of the muck truck and directly trace to the corresponding construction site.
Description
Technical Field
The invention relates to the technical field of traffic management, in particular to a specific vehicle traceability analysis method.
Background
At present, along with city modernization process is constantly accelerated, and urban construction is the blowout formula development, and removal building site and building earthwork transportation, rubbish dregs consumption increase sharply, then "dregs car is disorderly" has appeared, and the dregs car is left on the road after scattering, can't trace back which building site came out's vehicle, and law enforcement department can't manage the building site.
Disclosure of Invention
The invention provides a specific vehicle traceability analysis method which is used for solving the problems that a muck vehicle source cannot be traced and law enforcement departments cannot control a construction site.
As an embodiment of the invention: a specific vehicle traceability analysis method, comprising:
acquiring a vehicle scattering position and scattering time, judging whether the scattering position contains bayonet snapshot data or not based on a preset road traffic security bayonet monitoring system, and acquiring bayonet snapshot data corresponding to the muck vehicle;
carrying out feature analysis according to the bayonet snapshot data corresponding to the vehicle, and determining the features of the target vehicle;
and determining the source position of the target vehicle based on a preset muck vehicle capturing base and combining capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle.
As an embodiment of the invention: the data according to the bayonet socket snapshot that the dregs car corresponds carries out characteristic analysis, confirms target vehicle characteristic, includes:
capturing video frame data according to all bayonets corresponding to the muck truck to perform frame extraction processing, acquiring a plurality of frames of independent image frame data, pushing the image frame data to a corresponding algorithm through Redis to analyze, acquiring primary target vehicle characteristics, and determining the bayonet data containing the primary target vehicle characteristics as primary bayonet data;
pushing the primary bayonet data to Redis for secondary analysis, acquiring secondary target vehicle characteristics, and determining bayonet data containing the secondary target vehicle characteristics as secondary bayonet data;
performing data analysis on the secondary bayonet data to obtain structured data and determine tertiary bayonet data;
and synchronously pushing the three-level bayonet data to Kafka for feature extraction to obtain three-level target vehicle features, constructing a muck vehicle capture base library by utilizing an elastic search based on the three-level target vehicle features, and screening data based on the muck vehicle capture base library to obtain four-level target vehicle features.
As an embodiment of the invention: according to carry out characteristic analysis according to the bayonet socket candid photograph data that the dregs car corresponds, confirm target vehicle characteristic, still include:
acquiring snapshot video frame data corresponding to the scattering position according to the scattering position and the scattering time of the muck car;
carrying out video segmentation according to the snapshot video frame data corresponding to the muck truck to obtain a plurality of frames of image data;
performing target detection on the plurality of frames of image data, judging whether the vehicles in the image data are consistent with the corresponding muck vehicles, determining a judgment result, and acquiring target vehicles;
performing characteristic analysis on the target vehicle based on a preset characteristic analysis model to obtain basic characteristic information of the target vehicle; wherein the basic feature information includes: license plate information, vehicle color information, driver face information, and vehicle size information.
As an embodiment of the present invention: the method comprises the following steps of determining the source position of a target vehicle based on a preset muck vehicle capturing base and combined capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle, wherein the method comprises the following steps:
grouping the target vehicle characteristics based on a preset classification mode, retrieving snapshot data of the target vehicle based on the classification characteristics, and acquiring the corresponding snapshot data of the target vehicle within 24 hours;
carrying out data query on the snapshot data of the target vehicle within 24 hours based on a preset Oracle database to obtain GPS data of the target vehicle, carrying out multi-thread analysis on the GPS data of the target vehicle, and determining the target GPS data;
performing data fusion based on the target GPS data, the scattering time and the target vehicle snapshot points, determining a primary running track of the target vehicle, and judging the target position of the target vehicle according to the running track;
performing intelligent analysis by combining a road network based on the target position and the primary running track of the target vehicle to obtain a secondary running track of the target vehicle;
and performing information analysis based on the secondary running track and the target position of the target vehicle, and determining the source position of the target vehicle.
As an embodiment of the present invention: the method comprises the following steps of determining the source position of a target vehicle based on a preset muck vehicle capturing base and combined capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle, and further comprises the following steps:
based on a preset construction site information base, performing association analysis on longitude and latitude data corresponding to the GPS data and a spatial position corresponding to a construction site in the construction site information base to obtain an association analysis result;
performing data cleaning on the correlation analysis result to obtain abnormal data, performing removal processing on the abnormal data, and determining a target correlation analysis result; wherein the exception data comprises: the longitude and latitude data is 0, the association result is negative data, and the association result is more than 1;
and sequencing the analysis results of the target relevance according to the sequence from high to low, and taking the first 5 construction sites as primary source positions.
As an embodiment of the invention: the method comprises the following steps of determining the source position of a target vehicle based on a preset muck vehicle capturing base and combined with capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle, and further comprises the following steps:
acquiring GPS queue data according to the GPS data corresponding to the target vehicle, extracting and analyzing adjacent data in the GPS queue data, and acquiring an extraction and analysis result of the adjacent data of the GPS;
analyzing and tracing the extracted analysis result by combining the snapshot data of the target vehicle aiming at the running track of the target vehicle to obtain the track tracing result of the target vehicle;
and predicting the complete running track of the target vehicle based on a preset big data processing system aiming at the track tracing result of the target vehicle to obtain a prediction result of the complete track.
As an embodiment of the present invention: the method comprises the following steps of obtaining GPS queue data according to GPS data corresponding to the target vehicle, extracting and analyzing adjacent data in the GPS queue data, and obtaining an extraction and analysis result of the adjacent data of the GPS, wherein the execution steps comprise:
step 1: acquiring time corresponding to GPS data corresponding to a target vehicle, and acquiring a time difference value according to the time corresponding to the GPS data;
step 2: extracting longitude and latitude data based on the GPS queue data, acquiring two groups of longitude and latitude coordinate data, converting the longitude and latitude coordinate data, and acquiring longitude and latitude radian data; wherein the formula of coordinate transformation is Dxπ/180;
and step 3: and calculating the distance between the two groups of longitude and latitude coordinate data, wherein the calculation formula is as follows:
s＝6378.137×2×Math.asin(Math.sqrt(Math.pow(Math.sin(y 1 -y 2 )/2),2)
+Math.cos(y 1 )×Math.cos(y 2 )×Math.pow(Math.sin((x 1 -x 2 )/2),2)))
wherein x is 1 And y 1 Representing the result of a latitude-longitude conversion of a first set of GPS data, x 2 And y 2 Represents the longitude and latitude conversion result of the second group of GPS data, 6378.137 represents the variable of the radius of the earth;
and 4, step 4: calculating the speed according to the distance and time between the two groups of longitude and latitude coordinate data:
when the calculation result shows that the speed is more than 20m/s, automatic skipping processing is carried out,
when the calculation result shows that the time is 20min and the distance is 50m, the data is used as the optimal data,
and 5: and searching data based on the optimal data, acquiring a group containing construction site information, performing data analysis by combining snapshot point location data, and acquiring the running track of the target vehicle.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flowchart of a specific vehicle traceability analysis method according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
It is noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and that "plurality" means two or more than two unless expressly specified otherwise. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Example 1:
the embodiment of the invention provides a specific vehicle traceability analysis method, which comprises the following steps:
acquiring a vehicle scattering position and scattering time, judging whether the scattering position contains bayonet snapshot data or not based on a preset road traffic security bayonet monitoring system, and acquiring bayonet snapshot data corresponding to the muck vehicle;
carrying out characteristic analysis according to the bayonet snapshot data corresponding to the vehicle to determine the characteristics of the target vehicle;
determining the source position of the target vehicle based on a preset muck vehicle capturing base and combining capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle;
the principle of the implementation of the invention is as follows: the method adopts AI-based artificial intelligence technology to construct multi-algorithm capability, carries out double track study and judgment through bayonet snapping data and GPS data, analyzes vehicle foothold points, realizes accurate vehicle portrayal, and portrays each building site, each vehicle and each consumption place by utilizing building site data of the building committee, muck vehicle quasi-transport permit, consumption permit, vehicle track data and consumption place data of the city administration, vehicle householder data of the ecological environment bureau, traffic squad truck archive data and law enforcement six-team truck operation qualification permission data to form data chain tracking;
the beneficial effects of the above technical scheme are: the invention collects the data of all video devices within a corresponding time period by receiving the road scattering position and the approximate time provided by a user, automatically studies and judges whether the illegal vehicle enters or exits the construction site through the data collected by the camera device, is favorable for saving a large amount of manpower examination, carries out track double study and judgment by combining the bayonet snapshot data and the GPS data, is favorable for quickly and accurately acquiring the target construction site position and the source position of the corresponding muck vehicle, and is favorable for improving the accuracy of searching a target vehicle by carrying out characteristic analysis on the snapshot data.
Example 2:
in one embodiment, the performing feature analysis according to the bayonet snapshot data corresponding to the muck truck to determine the target vehicle feature includes:
capturing video frame data according to all bayonets corresponding to the muck truck to perform frame extraction processing, acquiring a plurality of frames of independent image frame data, pushing the image frame data to a corresponding algorithm through Redis to analyze, acquiring a primary target vehicle characteristic, and determining the bayonet data containing the primary target vehicle characteristic as primary bayonet data;
pushing the primary bayonet data to Redis for secondary analysis to obtain secondary target vehicle characteristics, and determining bayonet data containing the secondary target vehicle characteristics as secondary bayonet data;
performing data analysis on the secondary bayonet data to obtain structured data and determine tertiary bayonet data;
synchronously pushing the three-level bayonet data to Kafka for feature extraction to obtain three-level target vehicle features, constructing a muck vehicle capture base library by utilizing an elastic search based on the three-level target vehicle features, and screening data based on the muck vehicle capture base library to obtain four-level target vehicle features;
the principle of the implementation of the invention is as follows: according to the method, the scattering position and time of the muck truck are received, all video equipment in the time period in the range are subjected to frame extraction, the data are pushed to an algorithm through Redis to be analyzed, bayonet data which accord with characteristics are pushed to Redis to be analyzed for the second time, more detailed characteristics are extracted, after structured data are pushed to Kafka, the extracted detailed characteristics of a target vehicle are screened in a muck truck capture base constructed by using elastic search, and the characteristic information of the target vehicle which is scattered is obtained;
the beneficial effects of the above technical scheme are: according to the method, the data read-write speed is improved by performing algorithm analysis on the bayonet video data through Redis, and meanwhile, the method supports the storage of large data into a memory, so that the accuracy is high when the data are read, the response speed is improved, for common data, if the data are read from a database for the first time, the data can be directly read without accessing the database when the data are read later, the data processing efficiency is improved, the data processing safety is improved, and the accuracy and details of characteristics are improved by performing secondary analysis on the data, so that the finally obtained target characteristics have high trust.
Example 3:
in one embodiment, the performing feature analysis according to the bayonet snapshot data corresponding to the muck truck to determine the target vehicle feature further includes:
acquiring snapshot video frame data corresponding to the scattering position according to the scattering position and the scattering time of the muck car;
carrying out video segmentation according to the snapshot video frame data corresponding to the muck truck to obtain a plurality of frames of image data;
performing target detection on the plurality of frames of image data, judging whether the vehicles in the image data are consistent with the corresponding muck vehicles, determining a judgment result, and acquiring target vehicles;
performing characteristic analysis on the target vehicle based on a preset characteristic analysis model to obtain basic characteristic information of the target vehicle; wherein the basic feature information includes: license plate information, vehicle color information, driver face information, and vehicle size information;
the principle of the implementation of the invention is as follows: according to the method, the video frame data of the target vehicle are obtained through the big data by inputting the missing positions and the approximate time, but the video frame data occupy a larger memory and possibly contain too much invalid characteristic data, so that more resources are wasted during characteristic analysis;
the beneficial effects of the above technical scheme are: according to the method, the corresponding snapshot video frame data is obtained through the left-scattering position and the left-scattering time of the muck car, so that effective data can be obtained from huge data, the efficiency and the capacity of data extraction are improved, the resource waste can be reduced by performing video segmentation on the video frame data, the data processing efficiency is improved, and the obtained characteristics of the target vehicle have high reliability.
Example 4:
the embodiment of the invention provides a method for determining the source position of a target vehicle based on a preset muck vehicle capturing base database and combined with capturing data and GPS data of the target vehicle according to the characteristics of the target vehicle, which comprises the following steps:
grouping the target vehicle characteristics based on a preset classification mode, retrieving snapshot data of the target vehicle based on the classification characteristics, and acquiring the corresponding snapshot data of the target vehicle within 24 hours;
carrying out data query on the snapshot data of the target vehicle within 24 hours based on a preset Oracle database to obtain GPS data of the target vehicle, carrying out multi-thread analysis on the GPS data of the target vehicle, and determining the target GPS data;
performing data fusion based on the target GPS data, the scattering time and the target vehicle snapshot point positions, determining a primary running track of the target vehicle, and judging the target position of the target vehicle according to the running track;
performing intelligent analysis by combining a road network based on the target position and the primary running track of the target vehicle to obtain a secondary running track of the target vehicle;
performing information analysis based on the secondary running track and the target position of the target vehicle, and determining the source position of the target vehicle;
the principle of the implementation of the invention is as follows: according to the method, the capturing data of the corresponding muck truck on the same day is searched by grouping according to the detailed characteristics of the target vehicle obtained through secondary analysis, meanwhile, GPS data which are in accordance with the characteristic information of the target vehicle are inquired from an Oracle database, the position of a vehicle foot-placement position is analyzed from multiple dimensions according to time, the capturing point position of the muck truck and the GPS data, the detailed track of the muck truck is analyzed in combination with a road network, and a construction site source is locked by analyzing the position information of the muck truck in comparison with the position of the muck truck;
the beneficial effects of the above technical scheme are: the method is favorable for improving the effectiveness of characteristic analysis by classifying the acquired target vehicle characteristics, so that the reliability and the effectiveness of data obtained when snapshot data is searched according to the characteristics are higher, in addition, the time is set to be 24h, the continuity of data extraction is favorably improved, the GPS basically feeds back data once in 0.5 second or 1 second, so that a lot of data need to be analyzed, and the part related to the GPS data is completely analyzed by multithreading, so that the effect of rapidness and better is achieved.
Example 5:
in one embodiment, the determining, according to the characteristics of the target vehicle, the source position of the target vehicle based on a preset muck vehicle capturing base and by combining capturing data of the target vehicle and GPS data further includes:
based on a preset construction site information base, performing association analysis on longitude and latitude data corresponding to the GPS data and a spatial position corresponding to a construction site in the construction site information base to obtain an association analysis result;
performing data cleaning on the association degree analysis result to obtain abnormal data, performing removal processing on the abnormal data, and determining a target association degree analysis result; wherein the exception data comprises: the longitude and latitude data is 0, the association result is negative data, and the association result is more than 1;
sorting the analysis results of the target relevance degrees from high to low, and taking the first 5 construction sites as primary source positions;
the principle of the implementation of the invention is as follows: before GPS data is put in storage, the GPS data is compared with the existing construction site space position, a program draws a construction site polygon space range, the construction site space range is relatively expanded because the longitude and latitude data of the GPS is not very accurate, the longitude and latitude coordinates of the GPS are compared with the longitude and latitude coordinates of the GPS for calculation, whether the current longitude and latitude is in the construction site range is judged according to a space algorithm, and the time of subsequent analysis is shortened. Can clear up data before the analysis foothold, clear away those dirty data that appear the mistake, for example the longitude and latitude all is 0 data, present foothold divide into two kinds, firstly the street side is berthhed, secondly the building site is berthhed, and this scheme focus is berthhed for the building site, and foothold analysis can be followed time and this two dimensions in space and is analyzed.
The beneficial effects of the above technical scheme are: according to the method, the GPS data and the data in the construction site information base are subjected to association analysis, so that the data can be effectively screened from huge data quantity, the association analysis results are sorted, the data source searching is more targeted, the data are cleaned according to the association analysis results, abnormal data in data concentration can be cleared, and the accuracy and efficiency of data selection and processing are improved.
Example 6:
in one embodiment, the determining the source position of the target vehicle according to the target vehicle characteristics based on a preset muck vehicle capturing base library and by combining capturing data of the target vehicle and GPS data further includes:
acquiring GPS queue data according to the GPS data corresponding to the target vehicle, extracting and analyzing adjacent data in the GPS queue data, and acquiring an extraction and analysis result of the adjacent data of the GPS;
analyzing and tracing the extracted analysis result by combining the snapshot data of the target vehicle aiming at the running track of the target vehicle to obtain the track tracing result of the target vehicle;
predicting the complete running track of the target vehicle based on a preset big data processing system aiming at the track tracing result of the target vehicle to obtain a prediction result of the complete track;
the principle of the implementation of the invention is as follows: the method extracts every two adjacent data in the GPS data queue for analysis by the GPS data of the target vehicle, predicts partial tracks according to the analysis result, and intelligently analyzes the partial tracks based on a big data processing system to obtain a complete track prediction result;
the beneficial effects of the above technical scheme are: according to the method, the accuracy of obtaining the target characteristics is improved by carrying out characteristic analysis on the GPS data of the target vehicle, the monitoring and analysis of the whole operation flow of the muck vehicle are realized, powerful basis is provided for law enforcement departments to enforce law afterwards, the track of the target vehicle is completely predicted based on the big data processing platform, the reliability of the obtained prediction result is high, and the source position of the corresponding target vehicle can be quickly obtained.
Example 7:
the embodiment of the invention provides a method for acquiring GPS queue data according to GPS data corresponding to a target vehicle, extracting and analyzing adjacent data in the GPS queue data and acquiring a GPS adjacent data extraction and analysis result, which comprises the following steps:
step 1: acquiring time corresponding to GPS data corresponding to a target vehicle, and acquiring a time difference value according to the time corresponding to the GPS data;
step 2: extracting longitude and latitude data based on the GPS queue data, acquiring two groups of longitude and latitude coordinate data, converting the longitude and latitude coordinate data, and acquiring longitude and latitude radian data; wherein the formula of coordinate transformation is Dxπ/180;
and step 3: and calculating the distance between the two groups of longitude and latitude coordinate data, wherein the calculation formula is as follows:
s＝6378.137×2×Math.asin(Math.sqrt(Math.pow(Math.sin(y 1 -y 2 )/2),2) +Math.cos(y 1 )×Math.cos(y 2 )×Math.pow(Math.sin((x 1 -x 2 )/2),2)))
wherein x is 1 And y 1 Representing the result of a latitude-longitude conversion of a first set of GPS data, x 2 And y 2 Represents the longitude and latitude conversion result of the second group of GPS data, 6378.137 represents the variable of the radius of the earth;
and 4, step 4: calculating the speed according to the distance and time between the two groups of longitude and latitude coordinate data:
when the calculation result shows that the speed is more than 20m/s, automatic skipping processing is carried out,
when the calculation result shows that the time is 20min and the distance is 50m, the data is used as the optimal data,
and 5: performing data search based on the optimal data, acquiring a group containing construction site information, performing data analysis by combining snapshot point location data, and acquiring a running track of a target vehicle;
the principle of the implementation of the invention is as follows: the method comprises the steps of extracting and analyzing adjacent data in a GPS data queue related to a target vehicle pairwise, firstly, obtaining time difference by time difference of two groups of GPS data, then extracting longitude and latitude coordinates of the two groups of GPS data for conversion, converting the coordinate data into radian data, calculating the distance between the two groups of data, further calculating corresponding speed values, automatically skipping when the speed is greater than 20 meters per second, comparing the distance with the time when the speed is less than 20 meters per second, comparing and analyzing through a series of experiments, obtaining the optimal distance of 50 meters when the time is 20 minutes, logically judging and grouping the data, obtaining a group containing construction site information after screening out data which accords with logic, analyzing and tracing a driving track of the target vehicle by combining snapshot point data, realizing accurate portrait of the vehicle, summarizing after analyzing a large amount of data, and carrying out tracing prediction on vehicles which are not used;
the beneficial effects of the above technical scheme are: according to the method, double track study and judgment are carried out through the bayonet snapshot data and the GPS data, meanwhile, vehicle footfall points are analyzed, and accurate vehicle portrait is favorably realized.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A specific vehicle traceability analysis method is characterized by comprising the following steps:
acquiring a vehicle scattering position and scattering time, judging whether the scattering position contains bayonet snapshot data or not based on a preset road traffic police bayonet monitoring system, and acquiring bayonet snapshot data corresponding to the muck vehicle;
carrying out feature analysis according to the bayonet snapshot data corresponding to the vehicle, and determining the features of the target vehicle;
and determining the source position of the target vehicle based on a preset muck vehicle capturing base and by combining capturing data of the target vehicle and GPS data according to the characteristics of the target vehicle.
2. The method for analyzing the source of the specific vehicle according to claim 1, wherein the step of performing feature analysis according to the bayonet snapshot data corresponding to the vehicle to determine the target vehicle feature comprises the steps of:
capturing video frame data according to all bayonets corresponding to the muck truck to perform frame extraction processing, acquiring a plurality of frames of independent image frame data, pushing the image frame data to a corresponding algorithm through Redis to analyze, acquiring primary target vehicle characteristics, and determining the bayonet data containing the primary target vehicle characteristics as primary bayonet data;
pushing the primary bayonet data to Redis for secondary analysis, acquiring secondary target vehicle characteristics, and determining bayonet data containing the secondary target vehicle characteristics as secondary bayonet data;
performing data analysis on the secondary bayonet data to obtain structured data and determine tertiary bayonet data;
and synchronously pushing the three-level bayonet data to Kafka for feature extraction to obtain three-level target vehicle features, constructing a muck vehicle capture base library by utilizing an elastic search based on the three-level target vehicle features, and screening data based on the muck vehicle capture base library to obtain four-level target vehicle features.
3. The method for analyzing the source tracing of the specific vehicle according to claim 1, wherein the step of performing feature analysis according to the bayonet snapshot data corresponding to the vehicle to determine the target vehicle feature further comprises the steps of:
acquiring snapshot video frame data corresponding to the scattering position according to the scattering position and the scattering time of the muck car;
carrying out video segmentation according to the snapshot video frame data corresponding to the muck truck to obtain a plurality of frames of image data;
performing target detection on the plurality of frames of image data, judging whether the vehicles in the image data are consistent with the corresponding muck vehicles, determining a judgment result, and acquiring target vehicles;
performing characteristic analysis on the target vehicle based on a preset characteristic analysis model to obtain basic characteristic information of the target vehicle; wherein the basic feature information includes: license plate information, vehicle color information, driver face information, and vehicle size information.
4. The method for analyzing the source of the specific vehicle according to claim 1, wherein the determining the source position of the target vehicle based on the preset capturing base database of the muck vehicle and the capturing data and the GPS data of the target vehicle according to the target vehicle characteristics comprises:
grouping the target vehicle characteristics based on a preset classification mode, retrieving snapshot data of the target vehicle based on the classification characteristics, and acquiring the corresponding snapshot data of the target vehicle within 24 hours;
performing data query on the snapshot data of the target vehicle within 24 hours based on a preset Oracle database to obtain GPS data of the target vehicle, performing multi-thread analysis on the GPS data of the target vehicle, and determining the target GPS data;
performing data fusion based on the target GPS data, the scattering time and the target vehicle snapshot point positions, determining a primary running track of the target vehicle, and judging the target position of the target vehicle according to the running track;
performing intelligent analysis by combining a preset road network based on the target position and the primary running track of the target vehicle to obtain a secondary running track of the target vehicle;
and performing information analysis and tracing based on the secondary running track and the target position of the target vehicle, and determining the source position of the target vehicle.
5. The method for analyzing the source of the specific vehicle according to claim 1, wherein the determining the source position of the target vehicle according to the target vehicle feature based on a preset capturing base database of the muck vehicle and by combining capturing data of the target vehicle and GPS data further comprises:
based on a preset construction site information base, performing association analysis on longitude and latitude data corresponding to the GPS data and a spatial position corresponding to a construction site in the construction site information base to obtain an association analysis result;
performing data cleaning on the correlation analysis result to obtain abnormal data, performing removal processing on the abnormal data, and determining a target correlation analysis result; wherein the exception data comprises: the longitude and latitude data are 0 data, the association result is negative data, and the association result is more than 1 data;
and sequencing the analysis results of the target relevance according to the sequence from high to low, and taking the first 5 construction sites as primary source positions.
6. The method for analyzing the source of the specific vehicle according to claim 1, wherein the determining the source position of the target vehicle according to the target vehicle feature based on a preset capturing base database of the muck vehicle and by combining capturing data of the target vehicle and GPS data further comprises:
acquiring GPS queue data according to the GPS data corresponding to the target vehicle, extracting and analyzing adjacent data in the GPS queue data, and acquiring an extraction and analysis result of the adjacent data of the GPS;
analyzing and tracing the extracted analysis result by combining the snapshot data of the target vehicle aiming at the running track of the target vehicle to obtain the track tracing result of the target vehicle;
and predicting the complete running track of the target vehicle based on a preset big data processing system according to the track tracing result of the target vehicle to obtain a prediction result of the complete track.
7. The method for analyzing the source tracing of the specific vehicle according to claim 6, wherein the step of obtaining the GPS queue data according to the GPS data corresponding to the target vehicle, performing extraction analysis on adjacent data in the GPS queue data, and obtaining a result of the extraction analysis on the adjacent GPS data includes:
step 1: acquiring time corresponding to GPS data corresponding to a target vehicle, and acquiring a time difference value according to the time corresponding to the GPS data;
step 2: extracting longitude and latitude data based on the GPS queue data, acquiring two groups of longitude and latitude coordinate data, converting the longitude and latitude coordinate data, and acquiring longitude and latitude radian data; wherein, the formula of coordinate transformation is D multiplied by pi/180, D represents longitude and latitude data;
and 3, step 3: and calculating the distance between the two groups of longitude and latitude coordinate data, wherein the calculation formula is as follows:
s＝6378.137×2×Math.asin(Math.sqrt(Math.pow(Math.sin(y 1 -y 2 )/2),2)+Math.cos(y 1 )×Math.cos(y 2 )×Math.pow(Math.sin((x 1 -x 2 )/2),2)))
wherein x is 1 And y 1 Representing the result of a latitude-longitude conversion of a first set of GPS data, x 2 And y 2 Represents the result of latitude and longitude conversion of the second set of GPS data, 6378.137 represents the variable of the radius of the earth;
and 4, step 4: calculating the speed according to the distance and time between the two sets of longitude and latitude coordinate data:
when the calculation result shows that the speed is more than 20m/s, automatic skipping processing is carried out,
when the calculation result shows that the time is 20min and the distance is 50m, the data is used as the optimal data,
and 5: and searching data based on the optimal data, acquiring a group containing construction site information, analyzing the data by combining snapshot point location data, and acquiring the driving track of the target vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211081346.XA CN115457777B (en) | 2022-09-06 | 2022-09-06 | Specific vehicle traceability analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211081346.XA CN115457777B (en) | 2022-09-06 | 2022-09-06 | Specific vehicle traceability analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115457777A true CN115457777A (en) | 2022-12-09 |
| CN115457777B CN115457777B (en) | 2023-09-19 |
Family
ID=84302848
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211081346.XA Active CN115457777B (en) | 2022-09-06 | 2022-09-06 | Specific vehicle traceability analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115457777B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115964545A (en) * | 2023-03-16 | 2023-04-14 | 四川国蓝中天环境科技集团有限公司 | Method for deducing pollution point location based on slag transport vehicle track point |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201548995U (en) * | 2009-12-24 | 2010-08-11 | 济南大学 | Building waste soil transport management system |
| CN103473927A (en) * | 2013-09-18 | 2013-12-25 | 柳州天运寰通科技有限公司 | Monitoring method of vehicle leaking and sifting monitoring system |
| RU2014100605A (en) * | 2014-01-09 | 2015-07-20 | Государственное предприятие "Украинский научно-технический центр металлургической промышленности "Энергосталь" (ГП "УкрНТЦ "Энергосталь") | IRON AND STEEL WORKS |
| CN110092217A (en) * | 2019-03-27 | 2019-08-06 | 中铁隧道股份有限公司 | The monoblock type that dregs in tunnel in slag waggon compartment dumps is turned over into slag workbench |
| CN110889371A (en) * | 2019-11-26 | 2020-03-17 | 浙江大华技术股份有限公司 | Method and device for detecting throwing of muck truck |
| CN210244457U (en) * | 2019-10-10 | 2020-04-03 | 中电骏途新能源科技有限公司 | System for automatically confirming that muck vehicle goes out of construction site |
| CN111274982A (en) * | 2020-02-04 | 2020-06-12 | 浙江大华技术股份有限公司 | Method and device for identifying projectile and storage medium |
| CN112215038A (en) * | 2019-07-11 | 2021-01-12 | 上海闪马智能科技有限公司 | Specific vehicle identification system, method, and storage medium |
| CN112348091A (en) * | 2020-11-10 | 2021-02-09 | 成都福立盟环保大数据有限公司 | Dual-clustering blackwork site identification algorithm based on GPS (global positioning system) of muck truck |
| CN112432654A (en) * | 2020-11-20 | 2021-03-02 | 浙江大华汽车技术有限公司 | State analysis method and device for muck truck and storage medium |
| CN112733047A (en) * | 2020-11-05 | 2021-04-30 | 浙江大华技术股份有限公司 | Vehicle foothold generation method, device, equipment and computer storage medium |
| CN113688805A (en) * | 2021-10-25 | 2021-11-23 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle-based unlicensed muck vehicle identification method and system |
| CN113689102A (en) * | 2021-08-17 | 2021-11-23 | 武汉依迅北斗时空技术股份有限公司 | Construction site monitoring mode determining method and system |
| CN114023076A (en) * | 2021-10-22 | 2022-02-08 | 北京商海文天科技发展有限公司 | Specific vehicle tracking method based on multi-source heterogeneous data |
| WO2022083166A1 (en) * | 2020-10-19 | 2022-04-28 | 山东交通学院 | Method and system for reconstructing vehicle's driving trajectory when checkpoint data is missing |
| CN114529852A (en) * | 2022-02-21 | 2022-05-24 | 北京商海文天科技发展有限公司 | Video data-based carry-over detection and analysis method |
| WO2022105019A1 (en) * | 2020-11-20 | 2022-05-27 | 罗普特科技集团股份有限公司 | Snapshot quality evaluation method and apparatus for vehicle bayonet device, and readable medium |
| CN114692018A (en) * | 2022-03-22 | 2022-07-01 | 杭州海康威视系统技术有限公司 | Data processing method and device, electronic equipment and machine-readable storage medium |
-
2022
- 2022-09-06 CN CN202211081346.XA patent/CN115457777B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201548995U (en) * | 2009-12-24 | 2010-08-11 | 济南大学 | Building waste soil transport management system |
| CN103473927A (en) * | 2013-09-18 | 2013-12-25 | 柳州天运寰通科技有限公司 | Monitoring method of vehicle leaking and sifting monitoring system |
| RU2014100605A (en) * | 2014-01-09 | 2015-07-20 | Государственное предприятие "Украинский научно-технический центр металлургической промышленности "Энергосталь" (ГП "УкрНТЦ "Энергосталь") | IRON AND STEEL WORKS |
| CN110092217A (en) * | 2019-03-27 | 2019-08-06 | 中铁隧道股份有限公司 | The monoblock type that dregs in tunnel in slag waggon compartment dumps is turned over into slag workbench |
| CN112215038A (en) * | 2019-07-11 | 2021-01-12 | 上海闪马智能科技有限公司 | Specific vehicle identification system, method, and storage medium |
| CN210244457U (en) * | 2019-10-10 | 2020-04-03 | 中电骏途新能源科技有限公司 | System for automatically confirming that muck vehicle goes out of construction site |
| CN110889371A (en) * | 2019-11-26 | 2020-03-17 | 浙江大华技术股份有限公司 | Method and device for detecting throwing of muck truck |
| CN111274982A (en) * | 2020-02-04 | 2020-06-12 | 浙江大华技术股份有限公司 | Method and device for identifying projectile and storage medium |
| WO2022083166A1 (en) * | 2020-10-19 | 2022-04-28 | 山东交通学院 | Method and system for reconstructing vehicle's driving trajectory when checkpoint data is missing |
| CN112733047A (en) * | 2020-11-05 | 2021-04-30 | 浙江大华技术股份有限公司 | Vehicle foothold generation method, device, equipment and computer storage medium |
| CN112348091A (en) * | 2020-11-10 | 2021-02-09 | 成都福立盟环保大数据有限公司 | Dual-clustering blackwork site identification algorithm based on GPS (global positioning system) of muck truck |
| CN112432654A (en) * | 2020-11-20 | 2021-03-02 | 浙江大华汽车技术有限公司 | State analysis method and device for muck truck and storage medium |
| WO2022105019A1 (en) * | 2020-11-20 | 2022-05-27 | 罗普特科技集团股份有限公司 | Snapshot quality evaluation method and apparatus for vehicle bayonet device, and readable medium |
| CN113689102A (en) * | 2021-08-17 | 2021-11-23 | 武汉依迅北斗时空技术股份有限公司 | Construction site monitoring mode determining method and system |
| CN114023076A (en) * | 2021-10-22 | 2022-02-08 | 北京商海文天科技发展有限公司 | Specific vehicle tracking method based on multi-source heterogeneous data |
| CN113688805A (en) * | 2021-10-25 | 2021-11-23 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle-based unlicensed muck vehicle identification method and system |
| CN114529852A (en) * | 2022-02-21 | 2022-05-24 | 北京商海文天科技发展有限公司 | Video data-based carry-over detection and analysis method |
| CN114692018A (en) * | 2022-03-22 | 2022-07-01 | 杭州海康威视系统技术有限公司 | Data processing method and device, electronic equipment and machine-readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 陈璐;管霜霜;谢艳芳;: "基于树莓派与神经计算棒的特种车辆检测识别", 计算机系统应用, no. 09 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115964545A (en) * | 2023-03-16 | 2023-04-14 | 四川国蓝中天环境科技集团有限公司 | Method for deducing pollution point location based on slag transport vehicle track point |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115457777B (en) | 2023-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111429726B (en) | Monitoring video illegal parking vehicle detection and management method and corresponding system | |
| Arya et al. | Transfer learning-based road damage detection for multiple countries | |
| Yuan et al. | An unlicensed taxi identification model based on big data analysis | |
| CN103578277B (en) | Deck suspicion car searching method and device thereof | |
| CN107481511A (en) | A kind of method and system for calculating candidate bus station | |
| CN103294775A (en) | Police service cloud image recognition vehicle management and control system based on geographic space-time constraint | |
| CN104199903A (en) | Vehicle data query system and method based on path correlation | |
| Tung et al. | Large-scale object detection of images from network cameras in variable ambient lighting conditions | |
| CN115457777A (en) | Specific vehicle traceability analysis method | |
| CN115272300B (en) | Pavement disease detection method, system, device, equipment and medium | |
| CN114898297A (en) | Non-motor vehicle illegal behavior determination method based on target detection and target tracking | |
| Singh et al. | Intelligent traffic management system | |
| CN115203354A (en) | Vehicle code track pre-association method and device, computer equipment and storage medium | |
| CN114446287A (en) | NLP and GIS based urban event allocation method and system | |
| CN114418360A (en) | Smart city operation sign big data analysis method and device | |
| Kamenetsky et al. | Aerial car detection and urban understanding | |
| Kumar et al. | The Real Time Vehicle License Plate Identification System | |
| CN112651992A (en) | Trajectory tracking method and system | |
| CN116524210A (en) | Automatic driving data screening method, system, electronic equipment and storage medium | |
| CN110765900A (en) | DSSD-based automatic illegal building detection method and system | |
| CN115187886A (en) | Vehicle violation detection method and device and electronic equipment | |
| CN112289028B (en) | Method, device, equipment and medium for discovering fake license plate related to vehicle | |
| Adrees et al. | A proposed framework for using GIS to enhance traffic safety in Sudan: a case study | |
| CN114218383A (en) | Method, device and application for judging repeated events | |
| CN114090909A (en) | Graph code joint detection correlation method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |