CN116089638B - Calculation method based on vehicle graph code track multidimensional correlation real-time profiling - Google Patents
Calculation method based on vehicle graph code track multidimensional correlation real-time profiling Download PDFInfo
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Abstract
The application relates to the technical field of data processing, in particular to a calculation method for real-time profiling based on multi-dimensional association of vehicle image code tracks, which specifically comprises the following steps: accessing original track data in real time; checking original track data; grouping the track data passing the verification to obtain a data set; sequencing and dividing batches of the data sets to obtain data batch sets; screening to obtain other types of associated data sets; performing pairwise association on the data batch set and other types of associated data sets to obtain association relation files of any two types of data in the same space-time range; and storing the association relation file according to the day, and merging the results of each day to obtain a total association result. The application realizes real-time, efficient and accurate association profiling of massive vehicle, portrait and IMSI data in any selected time range, and provides data support for work.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a calculation method for real-time profiling based on multi-dimensional association of vehicle image code tracks.
Background
The technology of searching real-name person information based on license plates, mobile phones and face image data is a common technical means, but in the actual process, because user information, vehicle information, IMSI information and portrait information are all stored independently, data loss can occur only by adopting one technical means, and thus the problem that work is difficult to advance is caused. For example: basic information of a registered person is searched through real name information of a license plate or a mobile phone, but in actual life, the condition that the registered person and a user are not the same person often occurs, so that the result obtained by the method is quite possibly inaccurate; or searching real-name information by means of a face photo, but the premise of the method is that a video or a photo is needed, and if no picture data exists, the basic information of the real-name can not be found. Therefore, if the track data of the vehicle, the portrait and the IMSI information can be correlated and documented, the purpose of improving the data is achieved by utilizing the correlated and documented data complementation, and the method is beneficial to realizing the quick grasp of the identity and the track information of the real-name person.
The Nanjing Seak technology Limited liability company discloses a picture code matching filing method and medium based on mobile phone and portrait with application number CN202111623716.3, firstly preprocessing original portrait track data and mobile phone track data to generate portrait start time and portrait end time; carrying out matching analysis on the preprocessed portrait data and the preprocessed mobile phone data; then matching and scoring the processed data to generate a daily matching and scoring result; combining the daily matching scoring result and the historical matching scoring result to obtain a total matching scoring result, calculating the confidence coefficient of matching the portrait and the mobile phone number based on the total matching scoring result, and analyzing whether the portrait and the mobile phone number are documented or not according to the confidence coefficient result.
In the algorithm, the establishment of the data file relationship has stronger dependence on the matching scoring and the confidence result of each day, the file association relationship is not established in real time, and has longer hysteresis, and in practical application, if the data association files in a period of time before and after the corresponding viewing of the cases occur, the situation that no association or wrong association is likely to occur.
Disclosure of Invention
Aiming at the fact that the establishment of the data file relationship in the prior art has stronger dependence on the matching scoring and the confidence coefficient result of each day, the file association relationship is not established in real time, and has long-time hysteresis, in practical application, if the data association files are correspondingly checked in a period of time before and after the occurrence of the case, the situation that no association or wrong association is likely to occur is quite likely to happen.
In order to achieve the above object, the present application is realized by the following technical scheme:
a calculation method based on vehicle map code track multidimensional association real-time profiling comprises the following steps:
step 1: accessing original track data in real time;
step 2: checking original track data;
step 3: grouping the track data passing the verification to obtain a data set;
step 4: sequencing and dividing batches of the data sets to obtain data batch sets;
step 5: screening to obtain other types of associated data sets;
step 6: and carrying out pairwise association on the data batch set and other types of associated data sets to obtain association relation files of any two types of data in the same space-time range.
As a preferable scheme of the present application, in the step 1, the original track data is accessed in real time, specifically, the collected track data is accessed from Kafka in real time, and whether the track data is empty is judged, if yes, the track data is accessed again.
As a preferable scheme of the application, the original track data is divided into three data types, namely original portrait track data, original IMSI track data and original vehicle track data, and specifically comprises equipment type, equipment number, acquisition frequency, acquisition radius, acquisition time, real name identity information, face picture information, license plate picture information and IMSI information.
As a preferred scheme of the present application, in the step 2, the original track data is checked, and whether the value of the original track data meets the requirement and whether the acquisition time is the latest time are checked; the method specifically comprises the following steps: calculating the date of the track data according to the acquisition time, calculating an acquisition equipment combination of the track data according to the equipment number, and then calculating the latest data acquisition time of the current acquisition equipment combination in the date to judge whether the track data is overdue data, if the track data is overdue data, putting the track data into a waste queue, and if the track data is not overdue data, carrying out data caching; for the track data passing the verification, carrying out grouping cache according to the data type for real-time calculation; and carrying out backup storage according to the device type on track data which do not pass the verification, and using the track data as compensation calculation.
In the step 3, the track data passing through the verification is grouped, specifically, all track data in the cache are read, and the track data are respectively grouped according to the equipment numbers, so as to obtain a data set corresponding to each acquisition equipment.
In the step 4, sorting and batch dividing are performed on the data sets, specifically, traversing the data sets in the step 3, sorting track data acquired by the same acquisition device according to the acquisition time from small to large, and batch dividing is performed on the sorted track data according to the acquisition frequency of the acquisition device to obtain data batch sets of different track data; traversing the data batch set, performing de-duplication processing and resident data eliminating processing on the track data of the same batch to obtain a data batch set without repetition, and simultaneously obtaining a collection time range corresponding to the batch, namely the start time and the end time of batch collection.
In the step 5, as a preferred scheme of the present application, the screening to obtain other types of associated data sets specifically includes: and screening to obtain other types of associated equipment numbers within the effective acquisition radius range of the equipment according to one of the acquisition equipment numbers, screening to obtain data sets acquired by other types of associated equipment within the acquisition time range, namely other types of associated data sets, carrying out de-duplication processing and resident data eliminating processing on the other types of associated data sets to obtain other types of associated data sets without repeated data according to the other types of associated equipment numbers and the acquisition time range corresponding to batches.
As a preferred embodiment of the present application, the method further comprises: and storing the association relation file according to the day, and merging the results of each day to obtain a total association result.
Compared with the prior art, the application has the following beneficial effects: the method provided by the application carries out the pairwise gear establishment on the vehicle, the portrait and the IMSI data, has the advantages of high real-time performance, high reliability, strong controllability, dynamic adjustability and the like, and the association relation between the data can be dynamically adjusted according to the acquisition frequency and the effective acquisition range of each device.
The calculation method based on the multi-dimensional association of the vehicle image code track and the real-time filing provided by the application furthest reserves the original space-time states of the vehicle, the human face and the IMSI, restores the association states of the vehicle, the human face and the IMSI when collision occurs, and simultaneously utilizes a mechanism that the data batch carries out forward de-duplication on the collected data and carries out reverse rejection on the collected data according to resident data, thereby having strong robustness on the complex scene where the collected data resides and obtaining the correct association relation even if repeated collection is carried out for many times.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method in a preferred embodiment of the application;
FIG. 2 is a flow chart of data verification in a preferred embodiment of the present application;
fig. 3 is a flow chart of data processing in a preferred embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the application, fall within the scope of protection of the application.
Example 1: as shown in fig. 1-3, this embodiment provides a method for calculating a real-time profile based on multi-dimensional association of vehicle image code tracks, which includes:
step 1: accessing original track data in real time; the original track data is divided into three data types, namely original portrait track data, original IMSI track data and original vehicle track data, and specifically comprises equipment type, equipment number, acquisition frequency, acquisition radius, acquisition time, real-name identity information, face picture information, license plate picture information and IMSI information; and accessing the acquired track data from the Kafka in real time, judging whether the track data is empty, and re-accessing if the track data is empty.
Step 2: checking the original track data, and checking whether the value of the original track data meets the requirement and whether the acquisition time is the latest time; the method specifically comprises the following steps: calculating the date of the track data according to the acquisition time, calculating the acquisition equipment combination of the track data according to the equipment number, and then calculating the latest data acquisition time of the current acquisition equipment combination in the date to judge whether the track data is the expiration data, if so, putting the track data into a waste queue, and if not, carrying out data caching; for the track data passing the verification, carrying out grouping cache according to the data type for real-time calculation; and carrying out backup storage according to the device type on track data which do not pass the verification, and using the track data as compensation calculation.
Step 3: grouping the track data passing the verification; and reading all track data in the cache, and respectively grouping according to the equipment numbers to obtain a data set corresponding to each acquisition equipment.
Step 4: sorting and dividing batches of data sets; specifically, traversing the data set in the step 3, sorting the track data acquired by the same acquisition equipment from small to large according to the acquisition time, and dividing the sorted track data into batches according to the acquisition frequency of the acquisition equipment to obtain a data batch set of different track data; traversing the data batch set, performing de-duplication processing and resident data eliminating processing on the track data of the same batch to obtain a data batch set without repetition, and simultaneously obtaining a corresponding collection time range of the batch, namely the start time and the end time of batch collection.
Step 5: screening to obtain other types of associated data sets; and screening to obtain other types of associated equipment numbers within the effective acquisition radius range of the equipment according to one of the acquisition equipment numbers, screening to obtain data sets acquired by other types of associated equipment within the acquisition time range, namely other types of associated data sets, carrying out de-duplication processing and resident data eliminating processing on the other types of associated data sets to obtain other types of associated data sets without repeated data according to the other types of associated equipment numbers and the acquisition time range corresponding to batches.
Step 6: and carrying out pairwise association on the data batch set and other types of associated data sets to obtain association relation files of any two types of data in the same space-time range. And storing the association relation file according to the day, and merging the results of each day to obtain a total association result.
Example 2: referring to fig. 1-3 and the above method, the embodiment provides a method for calculating multidimensional association real-time profiling by taking portrait trace data as an example, which specifically comprises the following steps:
step 1: the method comprises the steps of accessing collected original portrait track data, IMSI track data and vehicle track data from Kafka in real time, wherein the original portrait track data, IMSI track data and vehicle track data specifically comprise equipment types, equipment numbers, collection frequencies, collection radiuses, collection time, real-name identity information, face picture information, license plate picture information and IMSI information; and judging whether the track data is empty or not, and re-accessing if the track data is empty.
Step 2: checking and filtering the original track data, checking whether the value of the original track data meets the requirement or not and whether the acquisition time is the latest time or not; calculating the date of the track data according to the acquisition time, calculating the acquisition equipment combination of the track data according to the equipment number, and then calculating the latest data acquisition time of the current acquisition equipment combination in the date to judge whether the track data is the expiration data, if so, putting the track data into a waste queue, and if not, carrying out data caching; for the track data passing the verification, carrying out grouping cache according to the data type for real-time calculation; and carrying out backup storage according to the device type on track data which do not pass the verification, and using the track data as compensation calculation.
Step 3: grouping the track data passing the verification; and reading all track data in the cache, and respectively grouping according to the equipment numbers to obtain a portrait data set, an IMSI data set and a vehicle data set which are acquired correspondingly by each acquisition equipment.
Step 4: traversing the portrait data set in the step 3, sorting portrait track data acquired by the same acquisition equipment from small to large according to acquisition time, and dividing batches of the sorted portrait track data according to acquisition frequency of the acquisition equipment to obtain portrait data batch sets of different track data; traversing the portrait data batch set, performing duplication elimination processing and resident data rejection processing on portrait track data of the same batch to obtain a portrait data batch set without repetition, and simultaneously obtaining a collection time range corresponding to the batch, namely the start time and the end time of batch collection.
Step 5: according to the number of one of the acquisition devices, screening to obtain the related device number of the vehicle track data and the IMSI track data in the radius range of the image acquisition device, according to the related device number and the acquisition time range corresponding to the batch, screening to obtain the vehicle data set and the IMSI data set acquired in the acquisition time range, and carrying out de-duplication processing and resident data eliminating processing on the vehicle data set and the IMSI data set to obtain the related vehicle data set and the related IMSI data set without repeated data.
Step 6: carrying out pairwise association on the portrait data batch set, the associated vehicle data set and the associated IMSI data set to obtain an association relation file of portrait data, license plates and IMSI; repeating the steps 4 to 5, and completely calculating all the portrait trace data.
And storing the association relation file according to the day, and merging the results of each day to obtain a total association result.
In summary, the method provided by the application carries out the pairwise gear establishment on the vehicle, the portrait and the IMSI data, has the advantages of high real-time performance, high reliability, strong controllability, dynamic adjustability and the like, and the association relation between the data can be dynamically adjusted according to the acquisition frequency and the effective acquisition range of each device.
The calculation method based on the multi-dimensional association of the vehicle image code track and the real-time filing provided by the application furthest reserves the original space-time states of the vehicle, the human face and the IMSI, restores the association states of the vehicle, the human face and the IMSI when collision occurs, and simultaneously utilizes a mechanism that the data batch carries out forward de-duplication on the collected data and carries out reverse rejection on the collected data according to resident data, thereby having strong robustness on the complex scene where the collected data resides and obtaining the correct association relation even if repeated collection is carried out for many times.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, may be embodied in whole or in part in the form of a computer program product comprising one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Any process or method description in a flowchart or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed in a substantially simultaneous manner or in an opposite order from that shown or discussed, including in accordance with the functions that are involved.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the methods of the embodiments described above may be performed by a program that, when executed, comprises one or a combination of the steps of the method embodiments, instructs the associated hardware to perform the method.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules described above, if implemented in the form of software functional modules and sold or used as a stand-alone product, may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (4)
1. The calculation method for real-time profiling based on multi-dimensional association of the vehicle image code tracks is characterized by comprising the following steps:
step 1: accessing original track data in real time;
step 2: checking original track data;
in the step 2, checking original track data, and checking whether the value of the original track data meets the requirement and whether the acquisition time is the latest time; the method specifically comprises the following steps: calculating the date of the track data according to the acquisition time, calculating an acquisition equipment combination of the track data according to the equipment number, and then calculating the latest data acquisition time of the current acquisition equipment combination in the date to judge whether the track data is overdue data, if the track data is overdue data, putting the track data into a waste queue, and if the track data is not overdue data, carrying out data caching; for the track data passing the verification, carrying out grouping cache according to the data type for real-time calculation; for track data which does not pass the verification, carrying out backup storage according to the equipment type and using the backup storage as compensation calculation;
step 3: grouping the track data passing the verification to obtain a data set;
in the step 3, grouping the track data passing the verification, specifically, reading all track data in the cache, and grouping according to the equipment numbers respectively to obtain a data set corresponding to each acquisition equipment;
step 4: sequencing and dividing batches of the data sets to obtain data batch sets;
in the step 4, sorting and batch dividing are performed on the data sets, specifically, traversing is performed on the data sets in the step 3, sorting is performed on the track data acquired by the same acquisition device according to the acquisition time from small to large, and batch dividing is performed on the sorted track data according to the acquisition frequency of the acquisition device, so as to obtain data batch sets of different track data; traversing the data batch set, performing de-duplication processing and resident data rejection processing on track data of the same batch to obtain a data batch set without repetition, and simultaneously obtaining a collection time range corresponding to the batch, namely the start time and the end time of batch collection;
step 5: screening to obtain other types of associated data sets;
in the step 5, screening to obtain other types of associated data sets specifically includes: screening to obtain other types of associated equipment numbers within the effective acquisition radius range of the equipment according to one of the acquisition equipment numbers, screening to obtain data sets acquired by other types of associated equipment within the acquisition time range, namely other types of associated data sets, carrying out de-duplication processing and resident data eliminating processing on the other types of associated data sets to obtain other types of associated data sets without repeated data according to the other types of associated equipment numbers and the acquisition time range corresponding to batches;
step 6: and carrying out pairwise association on the data batch set and other types of associated data sets to obtain association relation files of any two types of data in the same space-time range.
2. The method for calculating the multi-dimensional association real-time profiling based on the vehicle image code track according to claim 1, wherein in the step 1, original track data is accessed in real time, specifically, the collected track data is accessed from Kafka in real time, whether the track data is empty is judged, and if the track data is empty, the track data is accessed again.
3. The method for calculating the real-time profiling based on the multi-dimensional association of the vehicle image code track according to claim 2, wherein the original track data are divided into three data types, namely original portrait track data, original IMSI track data and original vehicle track data, and specifically comprise equipment type, equipment number, acquisition frequency, acquisition radius, acquisition time, real-name identity information, face picture information, license plate picture information and IMSI information.
4. The method for calculating the real-time profiling based on the multi-dimensional association of the vehicle image code tracks according to claim 1, further comprising: and storing the association relation file according to the day, and merging the results of each day to obtain a total association result.
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| CN111159254A (en) * | 2019-12-30 | 2020-05-15 | 武汉长江通信产业集团股份有限公司 | Big data processing-based vehicle and person association method |
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