CN114023076B - Specific vehicle tracking method based on multi-source heterogeneous data - Google Patents
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Abstract
The invention provides a specific vehicle tracking method based on multi-source heterogeneous data, which comprises the following steps: obtaining characteristic information of a suspected vehicle, and locking a specific range through a preset Gis technology and the characteristic information; intelligently screening and tracking monitoring equipment within a specific range, determining a screening result, judging whether a suspected vehicle appears in the screening result, and determining a judgment result; when the judgment result is that the suspect vehicle appears in the screening result, determining a target vehicle, and automatically tracking the route of the target vehicle through a preset road network analysis technology; and when the judgment result is that no suspect vehicle appears in the screening result and exceeds a preset time range, finishing the tracking of the monitoring equipment in the specific range.
Description
Technical Field
The invention relates to the technical field of multi-source heterogeneous data and vehicle tracking, in particular to a specific vehicle tracking method based on the multi-source heterogeneous data.
Background
The establishment of an efficient, fast and modern 'specific vehicle tracking system' is a requirement for situation development.
Disclosure of Invention
The invention provides a specific vehicle tracking method based on multi-source heterogeneous data to solve the problems.
The invention provides a specific vehicle tracking method based on multi-source heterogeneous data, which is characterized by comprising the following steps:
obtaining characteristic information of a suspected vehicle, and locking a specific range through a preset Gis technology and the characteristic information;
intelligently screening and tracking monitoring equipment in a specific range, determining a screening result, judging whether a suspected vehicle appears in the screening result, and determining a judgment result;
when the judgment result is that the suspect vehicle appears in the screening result, determining a target vehicle, and automatically tracking the route of the target vehicle through a preset road network analysis technology;
and when the judgment result is that no suspect vehicle appears in the screening result and exceeds a preset time range, finishing the tracking of the monitoring equipment in the specific range.
As an embodiment of the present technical solution, the feature information includes a case issue area through which a suspected vehicle passes, a case issue time of the suspected vehicle, and vehicle feature information of the suspected vehicle; wherein,
the vehicle characteristic information at least comprises one or more of vehicle type characteristic information, vehicle color characteristic information, vehicle license plate characteristic information, vehicle model characteristic information, vehicle year money characteristic information and vehicle skylight characteristic information.
As an embodiment of the present technical solution, the intelligently screening and tracking monitoring devices within a specific range, determining a screening result, and determining whether a suspect vehicle appears in the screening result, and determining a determination result, includes the following steps:
step 1: intelligently screening monitoring equipment in a specific range through a preset Gis technology, and determining a screening result;
step 2: acquiring a monitoring video of the monitoring equipment in the screening result, and performing frame extraction on the monitoring video to determine a frame extraction picture;
and step 3: judging whether the frame-extracted picture has a suspected vehicle or not based on a preset remote dictionary service and a tracking search mode, and determining a judgment result; wherein,
the tracking mechanism comprises a precise tracking mode and a fuzzy search mode; wherein,
the accurate tracking mode is used for accurately tracking the target vehicle through the license plate related file in the vehicle characteristic information or the vehicle license plate characteristic information;
the fuzzy search mode is used for carrying out fuzzy search on the target vehicle by combining part of vehicle characteristic information with a preset vehicle related file; wherein,
the vehicle-related records at least comprise one or more of vehicle records, vehicle annual audit information, vehicle illegal records and preset time limit vehicle landing places.
As an embodiment of the present technical solution, the step 1 further includes:
step 100: based on preset Gis technology and characteristic information, carrying out primary positioning on the search range, and determining a target area;
step 101: performing secondary area division on the target area to generate a key area and a non-key area;
step 102: receiving and analyzing monitoring equipment information of monitoring equipment in a key area, uniformly gathering the monitoring equipment information, uniformly converting formats of the monitoring equipment information, and determining uniform format information;
step 103: and based on the uniform format information, real-time docking and screening the monitoring equipment of the key area through a preset API (application program interface), and determining a screening result.
As an embodiment of the present technical solution, the step 3 further includes:
step 301: pushing the frame-extracted picture to a preset AI algorithm for analysis and identification to generate bayonet data according with the primary characteristics; <xnotran> , </xnotran>
The checkpoint data is used for all-weather real-time detection and recording vehicle driving data which accord with primary characteristics in a specific place;
step 302: pushing the bayonet data to a remote dictionary service for secondary analysis, structuring the bayonet data, and determining structured feature data;
step 303: and pushing the structural feature data to a preset Kafka system, comparing the structural feature data with feature information of suspected vehicles one by one, judging whether the suspected vehicles appear in the frame drawing picture, and determining a judgment result.
As an embodiment of the present technical solution, the determining a target vehicle when the determination result is that a suspect vehicle appears in the screening result includes the following steps:
step S1: when the judgment result shows that a single suspected vehicle appears, information acquisition is carried out on the suspected vehicle, and a target vehicle is determined;
step S2: when the judgment result shows that a plurality of suspected vehicles appear, a multitask tracking mechanism is constructed through a preset Kafka system and a DTC distributed computing system; wherein,
the Kafka system represents a distributed publishing and subscribing message system and is used for performing distributed publishing and subscribing on a plurality of calculation results of the DTC distributed calculation system;
the DTC distributed computing system is used for simultaneously performing distributed processing on at least one or more tasks of downloading pictures, identifying the pictures, putting the pictures into a warehouse, controlling the pictures and subscribing the pictures;
the multi-task tracking mechanism is used for tracking all suspected vehicles to generate a plurality of tracking tasks;
and step S3: and scheduling and managing the plurality of tracking tasks based on a preset DTC distributed computing system, locking a target vehicle, and transmitting the target vehicle to a preset control terminal.
As an embodiment of the present technical solution, the plurality of tracking tasks are divided into a task when the task is performed or a task when the task is completed; wherein,
when the tracking task is displayed as an end task, confirming a target vehicle, digitizing the end task, generating a corresponding task report, and identifying the tracking task as a finished state;
when the tracking task is displayed as a task in progress, generating a corresponding task list, and providing and displaying the task in the task list to perform corresponding functional operation; wherein,
the functional operation comprises continuing tracking, screening analysis or task ending.
As an embodiment of the present technical solution, the screening analysis further includes the following steps:
step A1: selecting vehicle characteristic information, screening and retrieving vehicles which do not finish tasks, and determining retrieval data;
step A2: displaying and retrieving corresponding state information of the vehicle corresponding to the data through a plurality of tracking tasks and the retrieved data; wherein,
the corresponding state information comprises license plate numbers, vehicle characteristics, state marks, appearance time and places;
step A3: performing secondary screening based on the corresponding state information to determine a target vehicle;
step A4: and transmitting the target vehicle and the corresponding vehicle information to a preset DTC distributed computing system, generating a screening report, and determining relevant information of the target vehicle through the screening report.
As an embodiment of the present technical solution, the automatically tracking a route of a target vehicle by a preset road network analysis technology further includes:
storing the target vehicle to a preset Mysql database, determining stored data, and meanwhile, carrying out pushing early warning through a preset WebSocket communication protocol;
collecting scheduling nodes through a preset DTC distributed computing system;
based on a preset distribution algorithm, slicing the stored data, determining sliced data, distributing the sliced data to a plurality of scheduling nodes for multidimensional calculation, and generating corresponding calculation results;
counting the calculation result to generate a statistical result, screening the statistical result, determining a target vehicle, and calling a related file of the target vehicle through an API (application program interface);
acquiring and displaying detailed information and associated information of a target vehicle, and generating corresponding judging information; wherein,
the study information at least comprises one or more of vehicle files, annual review information, illegal records and recent footfalls.
As an embodiment of the present invention, the specific vehicle tracking method further includes:
obtaining feature information of a suspected vehicle, extracting limited feature information of the feature information, and generating feature data according to the limited feature information; wherein,
the limiting characteristic information at least comprises one or more of vehicle structure information, vehicle type information, vehicle appearance information and vehicle license plate information;
constructing a characteristic model of a specific vehicle through the characteristic data, and collecting characteristic points on the characteristic model;
generating specific trigger characteristic data of the suspected vehicle based on the characteristic points, transmitting the specific trigger characteristic data to a preset event generator, and constructing a specific vehicle trigger acquisition mechanism;
triggering an acquisition mechanism based on the specific vehicle, butting the video of the monitoring equipment in real time, and identifying and acquiring images of suspected vehicles;
detecting whether the suspected vehicle image comprises preset unique vehicle identification information or not, and determining a detection result; wherein,
the vehicle unique identification information represents information having a vehicle unique identification;
when the detection result is that the suspected vehicle image comprises preset unique vehicle identification information, accurately tracking the suspected image, determining a target vehicle and generating a corresponding tracking report;
and when the detection result is that the suspected vehicle image does not include the preset unique vehicle identification information, acquiring the suspected vehicle image, sequentially retrieving and screening the suspected vehicle image based on a preset priority mechanism, detecting whether specific vehicle information exists or not, and determining a first detection result.
The invention has the following beneficial effects:
the technical scheme provides a specific vehicle tracking method based on multi-source heterogeneous data, which comprises the steps of collecting monitoring equipment information of different platforms in a preset area according to preset specific vehicle information; uniformly gathering monitoring equipment information, collecting illegal or suspected vehicle images in the monitoring equipment, accurately tracking the images of the vehicles, accurately searching the vehicles through unique identifiers such as vehicle IDs (identity), and if the characteristics of the vehicles are less and fuzzy, carrying out fuzzy search on the remaining target images so as to search out suspected targets; the method comprises the steps of sequentially searching and screening target images, judging whether specific violation or suspected vehicle information is submitted, collecting suspected vehicle data and transmitting the data to a control terminal when the vehicle information is only fuzzy, collecting equipment information corresponding to the specific vehicle data when the vehicle data is determined, and collecting driving information of the specific vehicle data and transmitting the driving information to the control terminal.
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 the 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 flowchart of a specific vehicle tracking method based on multi-source heterogeneous data according to an embodiment of the present invention;
FIG. 2 is a flowchart of a specific vehicle tracking method based on multi-source heterogeneous data according to an embodiment of the present invention;
FIG. 3 is a flowchart of a specific vehicle tracking method based on multi-source heterogeneous data 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 will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly connected to the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the device or element so referred to must be in a particular orientation, constructed or operated in a particular orientation, and is not to be construed as limiting the invention.
Moreover, 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 "a plurality" means two or more unless specifically limited 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:
according to the embodiment of the invention, as shown in fig. 1, a specific vehicle tracking method based on multi-source heterogeneous data is provided, and is characterized by comprising the following steps:
obtaining characteristic information of a suspected vehicle, and locking a specific range through a preset Gis technology and the characteristic information;
intelligently screening and tracking monitoring equipment within a specific range, determining a screening result, judging whether a suspected vehicle appears in the screening result, and determining a judgment result;
when the judgment result is that the suspect vehicle appears in the screening result, determining a target vehicle, and automatically tracking the route of the target vehicle through a preset road network analysis technology;
and when the judgment result is that the suspect vehicle does not appear in the screening result and exceeds the preset time range, finishing the tracking of the monitoring equipment in the specific range.
The working principle and the beneficial effects of the technical scheme are as follows:
the embodiment of the invention provides a specific vehicle tracking method based on multi-source heterogeneous data, which comprises the steps of obtaining characteristic information of a suspected vehicle, and locking a specific range through a preset Gis technology and the characteristic information; intelligently screening and tracking monitoring equipment within a specific range, determining a screening result, judging whether a suspected vehicle appears in the screening result, and determining a judgment result; when the judgment result is that the suspect vehicle appears in the screening result, the target vehicle is determined, the route of the target vehicle is automatically tracked through a preset road network analysis technology, the target can be accurately identified and positioned and tracked through two modes of accurate identification and fuzzy search, under the condition that the license plate number and the vehicle characteristics are not clear, the identification accuracy is low in a tracking scene under a low-pixel camera, the tracking target is easy to lose, and an accurate and flexible tracking mode is provided.
Example 2:
the technical scheme provides an embodiment, wherein the characteristic information comprises a case issue area where a suspected vehicle passes, case issue time of the suspected vehicle and vehicle characteristic information of the suspected vehicle; wherein,
the vehicle characteristic information at least comprises one or more of vehicle type characteristic information, vehicle color characteristic information, vehicle license plate characteristic information, vehicle model characteristic information, vehicle year money characteristic information and vehicle skylight characteristic information.
The working principle and the beneficial effects of the technical scheme are as follows:
the technical scheme includes that the characteristic information comprises a suspected vehicle passing case issuing area, suspected vehicle case issuing time and suspected vehicle characteristic information, the vehicle characteristic information at least comprises one or more of vehicle type characteristic information, vehicle color characteristic information, vehicle license plate characteristic information, vehicle model characteristic information, vehicle annual fee characteristic information and vehicle skylight characteristic information, the vehicle characteristic information is recorded, the vehicle is checked more accurately, and under the condition that no suspected vehicle license plate number exists, suspected vehicles can be screened out quickly and effectively and an efficient auxiliary screening function is provided only through less vehicle characteristic information.
Example 3:
as shown in fig. 2, the present technical solution provides an embodiment, where the intelligently screening and tracking monitoring devices within a specific range, determining a screening result, and determining whether a suspected vehicle appears in the screening result, and determining a determination result includes the following steps:
step 1: intelligently screening monitoring equipment in a specific range through a preset Gis technology, and determining a screening result;
step 2: acquiring a monitoring video of the monitoring equipment in the screening result, and performing frame extraction on the monitoring video to determine a frame extraction picture;
and step 3: judging whether the frame-extracted picture has a suspected vehicle or not based on a preset remote dictionary service and a tracking search mode, and determining a judgment result; wherein,
the tracking mechanism comprises a precise tracking mode and a fuzzy search mode; wherein,
the accurate tracking mode is used for accurately tracking the target vehicle through the license plate related file in the vehicle characteristic information or the vehicle license plate characteristic information;
the fuzzy search mode is used for carrying out fuzzy search on the target vehicle by combining part of vehicle characteristic information with a preset vehicle related file; wherein,
the vehicle-related records at least comprise one or more of vehicle records, vehicle annual audit information, vehicle illegal records and preset time limit vehicle landing places.
The working principle and the beneficial effects of the technical scheme are as follows:
the technical scheme is combined with a Gis technology, a camera is intelligently screened to track, suspect vehicles are screened, a case is taken as a center, multidimensional data fusion analysis is combined, whether suspect vehicles appear or not is analyzed based on a preset remote dictionary service and a tracking search mode, vehicle registration information, intelligent community access snapshot data, vehicle annual audit data and vehicle off-site violation data can be analyzed, a real-time video stream is docked through an automatic tracking system of an accurate tracking mode and a fuzzy search mode, the accurate tracking mode accurately tracks a historical video stream and a bayonet view library through license plate related files or vehicle license plate characteristic information in vehicle characteristic information, fuzzy characteristics of the suspect vehicles are screened and analyzed in a selected area, and the fuzzy search mode is used for carrying out fuzzy search on the target vehicles by combining part of the characteristic information with the preset vehicle related files; the suspected vehicle real-time studying and judging tracking is achieved through automatic retrieval of an algorithm system and man-machine cooperation, the tracking target is identified and positioned under the condition that the license plate number and the vehicle characteristics are unclear, and meanwhile, the lost tracking target can be found back due to low identification accuracy in a tracking scene under a low-pixel camera.
Example 4:
this technical scheme provides an embodiment, step 1, still include:
step 100: based on a preset Gis technology and characteristic information, carrying out primary positioning on the search range, and determining a target area;
step 101: performing secondary region division on the target region to generate a key region and a non-key region;
step 102: receiving and analyzing monitoring equipment information of monitoring equipment in a key area, uniformly gathering the monitoring equipment information, uniformly converting formats of the monitoring equipment information, and determining uniform format information;
step 103: and based on the uniform format information, real-time docking and screening the monitoring equipment of the key area through a preset API (application program interface), and determining a screening result.
The working principle and the beneficial effects of the technical scheme are as follows:
the technical scheme includes that the frame extraction picture is pushed to a preset AI algorithm to be analyzed and recognized, bayonet data which accord with primary characteristics are generated, the bayonet data are inspected in a vehicle by means of deep learning and mode recognition technology and distributed storage, a real-time stream processing platform is provided to process and analyze the vehicle data, an API (application program interface) is provided for analysis, study, judgment, control, early warning and business processing, relevant files of the vehicle are continuously pushed through the API in the screening and analyzing process, the suspected vehicle can be clicked to display detailed information and relevant information, the bayonet data are pushed to a remote dictionary service to be analyzed secondarily, the bayonet data are structured, structured characteristic data are determined, the bayonet data are unified, the data of the vehicle are structured through characteristic relations, the structured characteristic data are pushed to a preset Kafka system, the structural characteristic data are compared with characteristic information of the suspected vehicle one by one to one, whether the frame extraction picture has suspected vehicles is judged, a judgment result is determined, the video stream can be connected in real time, the information of the suspected vehicle is extracted, meanwhile, the distributed storage efficiency of the data is improved, and the storage cost of the data is reduced.
Example 5:
this technical scheme provides an embodiment, step 3, further includes:
step 301: pushing the frame-extracted picture to a preset AI algorithm for analysis and identification to generate bayonet data conforming to the primary characteristics; wherein,
the checkpoint data is used for all-weather real-time detection and recording vehicle driving data which accord with primary characteristics in a specific place;
step 302: pushing the bayonet data to a remote dictionary service for secondary analysis, structuring the bayonet data, and determining structured feature data;
step 303: and pushing the structural feature data to a preset Kafka system, comparing the structural feature data with feature information of suspected vehicles one by one, judging whether the suspected vehicles appear in the frame drawing picture, and determining a judgment result.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the technical scheme, when the judgment result is that the suspect vehicle appears in the screening result, the target vehicle is determined, when the judgment result is that a single suspect vehicle appears, information acquisition is carried out on the suspect vehicle, the target vehicle is determined, and the target vehicle is transmitted to a preset control terminal; when a plurality of suspected vehicles appear according to the judgment result, the DTC distributed computing system is used for simultaneously and distributively processing one or more tasks of at least downloading pictures, identifying pictures, warehousing the pictures, arranging and controlling the pictures and subscribing the pictures through a preset kafka system and a DTC distributed computing system, a multi-task tracking mechanism is constructed, large data research and judgment application can be completed only by using huge computing power, and if centralized computing is adopted, the consumed time is long. The distributed computing decomposes the application into a plurality of small parts which are distributed to a plurality of servers for processing, and the multi-task tracking mechanism is used for tracking all suspected vehicles to generate a plurality of tracking tasks, so that the overall computing time can be saved, and the computing efficiency is greatly improved. Under the condition of the technology, tasks can be switched randomly to study and judge or check task states, a plurality of tracking tasks are scheduled and managed based on a preset DTC distributed computing system, a target vehicle is locked and transmitted to a preset control terminal, and the distributed, extensible and fault-tolerant real-time computing system is provided.
Example 6:
the technical scheme provides an embodiment, when the judgment result is that the suspect vehicle appears in the screening result, the target vehicle is determined, and the method comprises the following steps:
step S1: when the judgment result shows that a single suspected vehicle appears, information acquisition is carried out on the suspected vehicle, and a target vehicle is determined;
step S2: when the judgment result shows that a plurality of suspected vehicles appear, a multitask tracking mechanism is constructed through a preset Kafka system and a DTC distributed computing system; wherein,
the Kafka system represents a distributed publishing and subscribing message system and is used for performing distributed publishing and subscribing on a plurality of calculation results of the DTC distributed calculation system;
the DTC distributed computing system is used for simultaneously performing distributed processing on at least one or more tasks of downloading pictures, identifying the pictures, putting the pictures into a warehouse, controlling the pictures and subscribing the pictures;
the multi-task tracking mechanism is used for tracking all suspected vehicles to generate a plurality of tracking tasks;
and step S3: and scheduling and managing the plurality of tracking tasks based on a preset DTC distributed computing system, locking a target vehicle, and transmitting the target vehicle to a preset control terminal.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the screening analysis of the technical scheme, vehicle characteristic information is selected, vehicles which do not finish tasks are screened and retrieved, retrieval data are determined, the vehicles which do not finish the tasks can be detected in time through the retrieval data, and corresponding state information of the vehicles corresponding to the retrieval data is displayed and retrieved through a plurality of tracking tasks and the retrieval data; wherein the corresponding state information comprises license plate number, vehicle characteristics, state marks, appearance time and place; performing secondary screening based on the corresponding state information to determine a target vehicle; and transmitting the target vehicle and the corresponding vehicle information to a preset DTC system, generating a screening report, and determining the relevant information of the target vehicle through the screening report.
Example 7:
the technical scheme provides an embodiment, wherein the plurality of tracking tasks are divided into a task in progress or a task ending; wherein,
when the tracking task is displayed as an end task, confirming a target vehicle, digitizing the end task, generating a corresponding task report, and identifying the tracking task as a finished state;
when the tracking task is displayed as a task in progress, generating a corresponding task list, and providing and displaying the task in the task list to perform corresponding functional operation; wherein,
the functional operation comprises continuing tracking, screening analysis or task ending.
The working principle and the beneficial effects of the technical scheme are as follows:
the multiple tracking tasks of the technical scheme are also used for digitizing the tracking process and generating a corresponding task list, when the tracking is carried out, the system can digitize the tracking task and provide the task list, one task can be selected to continue the operations of tracking, screening and analyzing and task ending, a user can conveniently and timely lock a man-machine cooperative target in the process of consuming too long time, the working efficiency is improved, and unnecessary time loss is avoided.
Example 8:
the technical scheme provides an embodiment, and the screening analysis further comprises the following steps:
step A1: selecting vehicle characteristic information, screening and retrieving vehicles which do not finish tasks, and determining retrieval data;
step A2: displaying and retrieving corresponding state information of the vehicle corresponding to the data through a plurality of tracking tasks and the retrieved data; wherein,
the corresponding state information comprises license plate numbers, vehicle characteristics, state marks, appearance time and places;
step A3: performing secondary screening based on the corresponding state information to determine a target vehicle;
step A4: and transmitting the target vehicle and the corresponding vehicle information to a preset DTC distributed computing system, generating a screening report, and determining the relevant information of the target vehicle through the screening report.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the technical scheme, a target vehicle is automatically tracked through a preset road network analysis technology, the target vehicle is stored in a preset Mysql database, stored data are determined, and meanwhile, pushing early warning is carried out through a preset WebSocket communication protocol; acquiring scheduling nodes through a preset DTC distributed computing system; based on a preset allocation algorithm, slicing the stored data, determining sliced data, allocating the sliced data to a plurality of scheduling nodes for multidimensional calculation, and generating corresponding calculation results; counting the calculation results to generate a statistical result, checking and screening the statistical result, and calling a related file of the target vehicle through an API (application program interface); acquiring and displaying detailed information and associated information of a target vehicle, and generating corresponding judging information; the study information includes at least one or more of vehicle records, annual audit information, illegal records and recent footfalls.
Example 9:
the technical solution provides an embodiment, where the automatic route tracking of the target vehicle is performed by a preset road network analysis technology, further comprising:
storing the target vehicle to a preset Mysql database, determining stored data, and meanwhile, carrying out pushing early warning through a preset WebSocket communication protocol;
acquiring scheduling nodes through a preset DTC distributed computing system;
based on a preset distribution algorithm, slicing the stored data, determining sliced data, distributing the sliced data to a plurality of scheduling nodes for multidimensional calculation, and generating corresponding calculation results;
counting the calculation result to generate a statistical result, checking and screening the statistical result to determine a target vehicle, and calling a related file of the target vehicle through an API (application program interface);
acquiring and displaying detailed information and associated information of a target vehicle, and generating corresponding study and judgment information; wherein,
the study information at least comprises one or more of vehicle files, annual review information, illegal records and recent footfalls.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the technical scheme, a more detailed characteristic result is extracted, structured data are pushed to a Kafka system, the Kafka is a distributed, partitioned, multi-copy and multi-subscriber, the distributed log system based on zookeeper coordination can be used for web/nginx logs, access logs, message services and the like, the structured data are pushed to the Kafka, can be distributed, calculated and stored in the multi-copy mode, and are shared by a plurality of subscribers, a background program consumes the Kafka and then is compared with the characteristics of a given target vehicle one by one, suspect vehicles meeting the characteristics fall to a Mysql database, meanwhile, a socket communication protocol is used for pushing and early warning, the pushed data are searched and screened in a man-machine cooperation mode, relevant files of the suspected vehicles are called continuously through an API interface in the screening and analyzing process, the suspect vehicles can be clicked to display detailed information and relevant information, the suspect vehicles comprise vehicle files, annual audit information, illegal records, recent ground-drop-in place and other important information. And finally, determining a target vehicle, drawing a traveling route through a road network analysis technology for automatic tracking, determining a target vehicle foot-landing place, and assigning personnel for processing.
Example 10:
this technical scheme provides an embodiment, still includes:
obtaining feature information of a suspected vehicle, extracting limited feature information of the feature information, and generating feature data according to the limited feature information; wherein,
the limiting characteristic information at least comprises one or more of vehicle structure information, vehicle type information, vehicle appearance information and vehicle license plate information;
constructing a characteristic model of a specific vehicle through the characteristic data, and collecting characteristic points on the characteristic model;
generating specific trigger characteristic data of the suspected vehicle based on the characteristic points, transmitting the specific trigger characteristic data to a preset event generator, and constructing a specific vehicle trigger acquisition mechanism;
triggering an acquisition mechanism based on the specific vehicle, butting the video of the monitoring equipment in real time, and identifying and acquiring images of suspected vehicles;
detecting whether the suspected vehicle image comprises preset unique vehicle identification information or not, and determining a detection result; wherein,
the vehicle unique identification information represents information having a vehicle unique identification;
when the detection result is that the suspected vehicle image comprises the preset unique vehicle identification information, accurately tracking the suspected image, determining a target vehicle and generating a corresponding tracking report;
and when the detection result is that the suspected vehicle image does not include the preset unique vehicle identification information, acquiring the suspected vehicle image, sequentially retrieving and screening the suspected vehicle image based on a preset priority mechanism, detecting whether specific vehicle information exists or not, and determining a first detection result.
The working principle and the beneficial effects of the technical scheme are as follows:
the technical scheme also comprises an embodiment, limited characteristic information of the characteristic information is extracted through obtaining the characteristic information of the suspected vehicle, corresponding characteristic data is generated, and accordingly the characteristic information is collected, the limited characteristic information at least comprises one or more of vehicle structure information, vehicle type information, vehicle appearance information and vehicle license plate information and is used for providing raw materials for constructing a vehicle model, the more specific the original data is, the more perfect the vehicle model is, specific trigger characteristic data of the suspected vehicle is generated by collecting characteristic points on the characteristic model and is transmitted to a preset event generator, a specific vehicle trigger collection mechanism is constructed, when the model runs and is matched, when the matching degree of the constructed vehicle model is greater than a preset threshold value, the specific vehicle trigger collection mechanism can be set out to identify videos which are in real time connected with monitoring equipment, so that an image of the suspected vehicle is collected, whether the image of the suspected vehicle comprises preset unique vehicle identification information or not is detected, a detection result is determined, when the detection result comprises the preset vehicle identification information on the image of the suspected vehicle, a corresponding accurate tracking report is generated, and a corresponding accurate tracking report is generated; and when the detection result is that the suspected vehicle image does not include the preset unique vehicle identification information, acquiring the suspected vehicle image based on a preset fuzzy search mode, sequentially searching and screening the suspected vehicle image based on a preset priority mechanism, detecting whether specific vehicle information exists, and determining a first detection result.
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 (9)
1. A specific vehicle tracking method based on multi-source heterogeneous data is characterized by comprising the following steps:
obtaining characteristic information of a suspected vehicle, and locking a specific range through a preset Gis technology and the characteristic information;
intelligently screening and tracking monitoring equipment within a specific range, determining a screening result, judging whether a suspected vehicle appears in the screening result, and determining a judgment result;
when the judgment result is that the suspect vehicle appears in the screening result, determining a target vehicle, and automatically tracking the route of the target vehicle through a preset road network analysis technology;
when the judgment result is that no suspect vehicle appears in the screening result and the time range exceeds the preset time range, ending the tracking of the monitoring equipment in the specific range;
the intelligent screening and tracking of the monitoring equipment in the specific range, determination of screening results, judgment of whether suspect vehicles appear in the screening results and determination of judgment results comprise the following steps:
step 1: intelligently screening monitoring equipment in a specific range through a preset Gis technology, and determining a screening result;
step 2: acquiring a monitoring video of the monitoring equipment in the screening result, and performing frame extraction on the monitoring video to determine a frame extraction picture;
and step 3: judging whether the frame-extracted picture has a suspected vehicle or not based on a preset remote dictionary service and a tracking search mode, and determining a judgment result; wherein,
the tracking mechanism comprises a precise tracking mode and a fuzzy search mode; wherein,
the accurate tracking mode is used for accurately tracking the target vehicle through the license plate related file in the vehicle characteristic information or the vehicle license plate characteristic information;
the fuzzy search mode is used for carrying out fuzzy search on the target vehicle by combining part of vehicle characteristic information with a preset vehicle related file; wherein,
the vehicle-related records at least comprise one or more of vehicle records, vehicle annual audit information, vehicle illegal records and preset time limit vehicle landing places.
2. The specific vehicle tracking method based on the multi-source heterogeneous data as claimed in claim 1, wherein the characteristic information comprises a case release area passed by a suspected vehicle, a case release time of the suspected vehicle, and vehicle characteristic information of the suspected vehicle; wherein,
the vehicle characteristic information at least comprises one or more of vehicle type characteristic information, vehicle color characteristic information, vehicle license plate characteristic information, vehicle model characteristic information, vehicle year money characteristic information and vehicle skylight characteristic information.
3. The specific vehicle tracking method based on multi-source heterogeneous data according to claim 2, wherein the step 1 further comprises:
step 100: based on preset Gis technology and characteristic information, carrying out primary positioning on the search range, and determining a target area;
step 101: performing secondary area division on the target area to generate a key area and a non-key area;
step 102: receiving and analyzing monitoring equipment information of monitoring equipment in a key area, uniformly gathering the monitoring equipment information, uniformly converting formats of the monitoring equipment information, and determining uniform format information;
step 103: and based on the uniform format information, real-time docking and screening the monitoring equipment of the key area through a preset API (application program interface), and determining a screening result.
4. The specific vehicle tracking method based on multi-source heterogeneous data according to claim 1, wherein the step 3 further comprises:
step 301: pushing the frame-extracted picture to a preset AI algorithm for analysis and identification to generate bayonet data conforming to the primary characteristics; wherein,
the checkpoint data is used for all-weather real-time detection and recording vehicle driving data which accord with primary characteristics in a specific place;
step 302: pushing the bayonet data to a remote dictionary service for secondary analysis, structuring the bayonet data, and determining structured feature data;
step 303: and pushing the structural feature data to a preset Kafka system, comparing the structural feature data with feature information of suspected vehicles one by one, judging whether the suspected vehicles appear in the frame drawing picture, and determining a judgment result.
5. The method for tracking the specific vehicle based on the multi-source heterogeneous data as claimed in claim 1, wherein when the determination result is that the suspected vehicle appears in the screening result, the target vehicle is determined, comprising the following steps:
step S1: when the judgment result is that a single suspect vehicle appears, information acquisition is carried out on the suspect vehicle, and a target vehicle is determined;
step S2: when the judgment result shows that a plurality of suspected vehicles appear, a multitask tracking mechanism is constructed through a preset Kafka system and a DTC distributed computing system; wherein,
the Kafka system represents a distributed publishing and subscribing message system and is used for performing distributed publishing and subscribing on a plurality of calculation results of the DTC distributed calculation system;
the DTC distributed computing system is used for simultaneously and distributively processing one or more tasks of at least downloading pictures, identifying pictures, warehousing the pictures, controlling the pictures and subscribing the pictures;
the multi-task tracking mechanism is used for tracking all suspected vehicles to generate a plurality of tracking tasks;
and step S3: and scheduling and managing the plurality of tracking tasks based on a preset DTC distributed computing system, locking a target vehicle, and transmitting the target vehicle to a preset control terminal.
6. The specific vehicle tracking method based on the multi-source heterogeneous data is characterized in that the plurality of tracking tasks are divided into an on-going task or an end task; wherein,
when the tracking task is displayed as an end task, confirming a target vehicle, digitizing the end task, generating a corresponding task report, and identifying the tracking task as a finished state;
when the tracking task is displayed as a task in progress, generating a corresponding task list, and providing and displaying the task in the task list to perform corresponding functional operation; wherein,
the functional operation comprises continuing tracking, screening analysis or task ending.
7. The method for tracking specific vehicles based on multi-source heterogeneous data according to claim 6, wherein the screening analysis further comprises the following steps:
step A1: selecting vehicle characteristic information, screening and retrieving vehicles which do not finish tasks, and determining retrieval data;
step A2: displaying and retrieving corresponding state information of the vehicle corresponding to the data through a plurality of tracking tasks and the retrieved data; wherein,
the corresponding state information comprises a license plate number, vehicle characteristics, a state mark, appearance time and place;
step A3: performing secondary screening based on the corresponding state information to determine a target vehicle;
step A4: and transmitting the target vehicle and the corresponding vehicle information to a preset DTC distributed computing system, generating a screening report, and determining relevant information of the target vehicle through the screening report.
8. The method for tracking specific vehicles based on multi-source heterogeneous data according to claim 1, wherein the automatic route tracking of the target vehicle is performed through a preset road network analysis technology, and further comprising:
storing the target vehicle to a preset Mysql database, determining stored data, and meanwhile, carrying out pushing early warning through a preset WebSocket communication protocol;
collecting scheduling nodes through a preset DTC distributed computing system;
based on a preset distribution algorithm, slicing the stored data, determining sliced data, distributing the sliced data to a plurality of scheduling nodes for multidimensional calculation, and generating corresponding calculation results;
counting the calculation result to generate a statistical result, checking and screening the statistical result to determine a target vehicle, and calling a related file of the target vehicle through an API (application program interface);
acquiring and displaying detailed information and associated information of a target vehicle, and generating corresponding judging information; wherein,
the study information at least comprises one or more of vehicle files, annual audit information, illegal records and recent footfalls.
9. A specific vehicle tracking method applied to any one of claims 1 to 8, further comprising:
obtaining feature information of a suspected vehicle, extracting limited feature information of the feature information, and generating feature data according to the limited feature information; wherein,
the limiting characteristic information at least comprises one or more of vehicle structure information, vehicle type information, vehicle appearance information and vehicle license plate information;
constructing a characteristic model of the specific vehicle according to the characteristic data, and collecting characteristic points on the characteristic model;
generating specific trigger characteristic data of the suspected vehicle based on the characteristic points, transmitting the specific trigger characteristic data to a preset event generator, and constructing a specific vehicle trigger acquisition mechanism;
triggering an acquisition mechanism based on the specific vehicle, butting the video of the monitoring equipment in real time, and identifying and acquiring images of suspected vehicles;
detecting whether the suspected vehicle image comprises preset unique vehicle identification information or not, and determining a detection result; wherein,
the vehicle unique identification information represents information having a vehicle unique identification;
when the detection result is that the suspected vehicle image comprises the preset unique vehicle identification information, accurately tracking the suspected image, determining a target vehicle and generating a corresponding tracking report;
and when the detection result is that the suspected vehicle image does not include the preset unique vehicle identification information, acquiring the suspected vehicle image, sequentially retrieving and screening the suspected vehicle image based on a preset priority mechanism, detecting whether specific vehicle information exists or not, and determining a first detection result.
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