CN112231509A - Big data-based automobile comparison analysis system - Google Patents

Big data-based automobile comparison analysis system Download PDF

Info

Publication number
CN112231509A
CN112231509A CN202011461510.0A CN202011461510A CN112231509A CN 112231509 A CN112231509 A CN 112231509A CN 202011461510 A CN202011461510 A CN 202011461510A CN 112231509 A CN112231509 A CN 112231509A
Authority
CN
China
Prior art keywords
vehicle
big data
pixel value
information
module
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
Application number
CN202011461510.0A
Other languages
Chinese (zh)
Other versions
CN112231509B (en
Inventor
邵洪飞
沈诫
王亚刚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhongguancun Technology Leasing Co ltd
Original Assignee
Hangzhou Yijiqingchen Information Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hangzhou Yijiqingchen Information Technology Co ltd filed Critical Hangzhou Yijiqingchen Information Technology Co ltd
Priority to CN202011461510.0A priority Critical patent/CN112231509B/en
Publication of CN112231509A publication Critical patent/CN112231509A/en
Application granted granted Critical
Publication of CN112231509B publication Critical patent/CN112231509B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/53—Querying
    • G06F16/535—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5846—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using extracted text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/63—Scene text, e.g. street names
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/625—License plates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/08—Detecting or categorising vehicles

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Library & Information Science (AREA)
  • Multimedia (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Tourism & Hospitality (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an automobile comparison and analysis system based on big data, which relates to the technical field of automobile analysis and solves the technical problems that the searching precision is not high and the advantages of the big data cannot be fully exerted in the automobile analysis and searching process; the invention sets the business terminal module, the setting comprises a public business terminal unit and a private terminal unit, the business terminal module separates the public from the private, thereby not only ensuring the accuracy of the public terminal unit for vehicle comparison, but also avoiding the complicated process of the private terminal unit for vehicle comparison, and being beneficial to improving the working efficiency of the invention; the invention is provided with a comparison analysis module which is used for analyzing the initially selected vehicle, the comparison analysis module analyzes the vehicle characteristic information in sequence, and each step in the analysis process generates a corresponding analysis result, thereby ensuring that a service terminal module can obtain an accurate comparison result; the invention is provided with the passerby reward module, the advantage of big data is fully exerted, and the search rate of the vehicle is improved.

Description

Big data-based automobile comparison analysis system
Technical Field
The invention belongs to the field of automobile analysis, relates to a big data processing technology, and particularly relates to an automobile comparative analysis system based on big data.
Background
In the current society, clothes and food are essential for people to live, and automobiles play an increasingly important role in daily life, so that automobile comparative analysis plays an important role in the fields of automobile tracking, automobile screening and the like.
The invention patent with the publication number of CN111291616A provides a paint surface ratio analysis system, which comprises a picture collection terminal, wherein the picture collection terminal is connected with a picture storage module, the picture storage module is connected with a paint surface analysis module, the paint surface analysis module is connected with a basic information input module, the basic information input module is connected with a big data platform, the big data platform is connected with a paint surface comparison module, the paint surface comparison module is connected with a paint surface processing result evaluation module, and the paint surface processing result evaluation module is connected with a client.
According to the scheme, the paint surface can be directly evaluated by only using the paint surface picture acquired by the phase acquisition terminal through the system, and a professional is not required to evaluate, so that manpower and material resources are saved; however, the above scheme only analyzes from the paint surface of the automobile, so that the analysis content is single, the accuracy of the analysis process is not high, and the big data technology is not fully utilized; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an automobile comparative analysis system based on big data.
The purpose of the invention can be realized by the following technical scheme: a big data-based automobile comparison and analysis system comprises a big data platform, a data storage module, a comparison and analysis module, a service terminal module and a passerby appreciation module;
the big data platform is respectively in linear connection with the data storage module and the comparative analysis module; the big data platform is respectively in communication connection with the service terminal module and the passerby appreciation module; the comparison analysis module is wirelessly connected with the service terminal module; the service terminal module comprises a public service terminal unit and a private terminal unit;
the service terminal module is used for sending vehicle characteristic information to the big data platform through the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and a tablet computer; the vehicle characteristic information comprises vehicle characteristic information of a suspected vehicle and vehicle characteristic information of a free vehicle;
the big data platform performs primary screening according to the vehicle characteristic information to obtain a primary vehicle, and sends the primary vehicle to the comparison and analysis module;
the passerby appreciation module is used for searching vehicles by passerby through appreciation signals and suspicion information; the reward signal and the suspicion information are obtained through a big data platform; the suspicion information comprises first suspicion information and second suspicion information;
and the comparison analysis module is used for analyzing the primarily selected vehicle to obtain an analysis result and feeding the analysis result back to the service terminal module and the big data platform respectively.
Preferably, the official terminal unit is used for sending the vehicle characteristic information of the suspect vehicle to the big data platform, and the big data platform obtains the vehicle of primary election according to the vehicle characteristic information of the suspect vehicle, including:
the official personnel carry out identity authentication through the intelligent terminal; the official staff is a state organ staff;
after the official terminal unit receives the identity verification success signal, vehicle characteristic information of the suspected vehicle is sent to the big data platform through the intelligent terminal; the vehicle characteristic information of the suspected vehicle comprises a vehicle type, a characteristic number, a vehicle image, a license plate number and vehicle owner information; the characteristic numbers comprise engine numbers and frame numbers; the vehicle types include passenger cars and cargo cars; the vehicle owner information comprises a vehicle owner name, a vehicle owner address and a mobile phone number for real-name authentication of the vehicle owner;
after receiving the vehicle characteristic information of the suspected vehicle, the big data platform acquires a characteristic information comparison library through the data storage module;
searching in the characteristic information comparison library by taking the frame number of the suspected vehicle as a search keyword, and marking the corresponding vehicle in the characteristic information comparison library as a primary vehicle when the vehicle corresponding to the frame number is searched; when the vehicle corresponding to the frame number is not searched, a data missing signal is sent to a business terminal unit through a big data platform;
the method comprises the steps that a primary vehicle is sent to a comparison analysis module through a big data platform, and meanwhile an appreciation signal and first suspicion information are sent to a passerby appreciation module; the first suspected information is vehicle characteristic information of a suspected vehicle.
Preferably, the private terminal unit is used for sending the vehicle characteristic information of the vehicle owned by the user to the big data platform through the intelligent terminal, and the big data platform acquires the vehicle to be initially selected according to the vehicle characteristic information of the vehicle owned by the user;
the user performs identity authentication through the intelligent terminal;
after receiving the identity verification success signal, the private terminal unit sends the vehicle characteristic information of the free vehicle to the big data platform through the intelligent terminal; the vehicle characteristic information of the own vehicle of the user comprises a vehicle type, a characteristic number, a vehicle image and a license plate number;
and after receiving the vehicle characteristic information of the own vehicle, the big data platform sends an appreciation signal and second suspicion information to the passerby appreciation module, wherein the second suspicion information comprises the vehicle characteristic information of the free vehicle.
Preferably, the comparative analysis module is configured to analyze the primary vehicle, and includes:
after the comparison analysis module receives the primary vehicle, comparing the engine number of the suspect vehicle with the engine number corresponding to the primary vehicle, and performing the next step when the comparison results are consistent; when the comparison results are inconsistent, the suspect vehicle is marked as an assembled vehicle, and a vehicle assembling signal is sent to the official terminal unit through the big data platform;
matching the vehicle types and license plate numbers of the suspected vehicle and the initially selected vehicle, and performing the next step when the matching results are consistent; when at least one matching result is inconsistent, marking the suspected vehicle as a modified vehicle, and sending a modified vehicle signal to the official terminal unit through the big data platform;
the method comprises the following steps of analyzing a vehicle image of a suspected vehicle and a vehicle image of a vehicle selected primarily, wherein the specific analysis steps comprise:
preprocessing the image of the suspected vehicle to obtain a first vehicle image, and preprocessing the image of the primarily selected vehicle to obtain a second vehicle image; the image preprocessing comprises image cutting, image enhancement, image denoising and gray level transformation;
acquiring pixel value curves of a first vehicle image and a second vehicle image, and respectively marking the pixel value curves as a first pixel value curve and a second pixel value curve;
deriving the first pixel value curve and the second pixel value curve to obtain the number of inflection points of the first pixel value curve and the number of inflection points of the second pixel value curve; when the number of the first pixel value curve inflection points is not equal to that of the second pixel value curve inflection points, the suspect vehicle is marked as a color-changing vehicle; when the number of the inflection points of the first pixel value curve is equal to that of the inflection points of the second pixel value curve, marking the inflection points as
Figure DEST_PATH_IMAGE002
，
Figure DEST_PATH_IMAGE003
=1，2，…，
Figure DEST_PATH_IMAGE005
；
Obtaining a first pixel value curve inflection point pixel value sequence
Figure DEST_PATH_IMAGE007
、
Figure DEST_PATH_IMAGE009
、…、
Figure DEST_PATH_IMAGE011
Wherein
Figure DEST_PATH_IMAGE013
Is a point of inflection
Figure DEST_PATH_IMAGE014
Corresponding pixel values in the first pixel value curve; obtaining a second pixel value curve inflection point pixel value sequence
Figure DEST_PATH_IMAGE016
、
Figure DEST_PATH_IMAGE018
、…、
Figure DEST_PATH_IMAGE020
Wherein
Figure DEST_PATH_IMAGE022
Is a point of inflection
Figure DEST_PATH_IMAGE023
A corresponding pixel value in the second pixel value curve;
by the formula
Figure DEST_PATH_IMAGE025
Obtaining an inflection point pixel value evaluation coefficient
Figure 100002_DEST_PATH_IMAGE027
(ii) a Wherein
Figure 100002_DEST_PATH_IMAGE029
And
Figure DEST_PATH_IMAGE031
is a predetermined scale factor, and
Figure DEST_PATH_IMAGE032
and
Figure DEST_PATH_IMAGE033
all are real numbers greater than 0;
when-inflection-point pixel value evaluation coefficient
Figure DEST_PATH_IMAGE034
Satisfy the requirement of
Figure DEST_PATH_IMAGE036
When the vehicle image matching result is consistent, the vehicle image matching result of the suspect vehicle and the vehicle image matching result of the primary vehicle are judged, and the vehicle owner information corresponding to the suspect vehicle is sent to the official terminal unit through the big data platform; when-inflection-point pixel value evaluation coefficient
Figure DEST_PATH_IMAGE037
Satisfy the requirement of
Figure DEST_PATH_IMAGE039
If so, judging that the matching result of the vehicle image of the suspected vehicle is inconsistent with the matching result of the vehicle image of the initially selected vehicle, marking the suspected vehicle as a color-changing vehicle, and sending a vehicle color-changing signal to a business terminal unit through a big data platform; wherein
Figure DEST_PATH_IMAGE041
Evaluating a coefficient threshold for a preset inflection point pixel value;
and sending the vehicle owner information sending record to a data storage module for storage.
Preferably, passerby rewards gold module sends reward gold signal to passerby's intelligent terminal, and passerby seeks the vehicle through suspicion information, includes:
before the reward signal is sent to the intelligent terminal of the passerby, the passerby needs to be registered through the intelligent terminal, and the passerby logs in after the registration is successful;
receiving suspicion information through an intelligent terminal after the passerby successfully logs in;
when a passerby encounters a vehicle with the same vehicle type, vehicle image and license plate number as those in the suspicion information, acquiring encountered vehicle detail information and sending the encountered vehicle detail information to a passerby appreciation module; the vehicle detail information comprises a detail image and a vehicle position of the vehicle;
the passerby appreciation module compares and matches the detailed images of the vehicles with the images of the vehicles in the suspicion information; when the comparison and matching results are consistent, the vehicle detail information is sent to a big data platform, and meanwhile, points are rewarded for passers-by; otherwise, the information inconsistency signal is sent to the intelligent terminal of the passerby through the passerby appreciation module.
Preferably, the step of obtaining the first pixel value curve includes:
acquiring pixel points of a first vehicle image and pixel values of corresponding pixel points, and performing descending order arrangement on the pixel values to acquire a pixel value arrangement table; the pixel value arrangement table comprises pixel values and pixel points corresponding to the pixels;
obtaining a pixel point sequence through a pixel value arrangement table, wherein the pixel point sequence is
Figure DEST_PATH_IMAGE043
、
Figure DEST_PATH_IMAGE045
、…、
Figure DEST_PATH_IMAGE047
(ii) a Wherein
Figure DEST_PATH_IMAGE049
The serial number of the pixel point is the serial number,
Figure DEST_PATH_IMAGE051
is a pixel point
Figure DEST_PATH_IMAGE052
Corresponding pixel value, and
Figure DEST_PATH_IMAGE054
；
and fitting a curve according to the pixel point sequence and marking the fitted curve as a first pixel value curve.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention sets up the terminal module of the business, this arrangement includes terminal unit of the public affairs and private terminal unit; the official personnel carry out identity authentication through the intelligent terminal; after the official terminal unit receives the identity verification success signal, vehicle characteristic information of the suspected vehicle is sent to the big data platform through the intelligent terminal; after receiving the vehicle characteristic information of the suspected vehicle, the big data platform acquires a characteristic information comparison library through the data storage module; searching in the characteristic information comparison library by taking the frame number of the suspected vehicle as a search keyword, and marking the corresponding vehicle in the characteristic information comparison library as a primary vehicle when the vehicle corresponding to the frame number is searched; when the vehicle corresponding to the frame number is not searched, a data missing signal is sent to a business terminal unit through a big data platform; the method comprises the steps that a primary vehicle is sent to a comparison analysis module through a big data platform, and meanwhile an appreciation signal and first suspicion information are sent to a passerby appreciation module; the service terminal module separates the public from the private, thereby not only ensuring the accuracy of the public terminal unit for vehicle comparison, but also avoiding the complicated process of the private terminal unit for vehicle comparison, and being beneficial to improving the working efficiency of the invention;
2. the invention is provided with a comparison analysis module, and the comparison analysis module is used for analyzing the primary vehicle; after the comparison analysis module receives the primary vehicle, comparing the engine number of the suspect vehicle with the engine number corresponding to the primary vehicle, and performing the next step when the comparison results are consistent; when the comparison results are inconsistent, the suspect vehicle is marked as an assembled vehicle, and a vehicle assembling signal is sent to the official terminal unit through the big data platform; matching the vehicle types and license plate numbers of the suspected vehicle and the initially selected vehicle, and performing the next step when the matching results are consistent; when at least one matching result is inconsistent, marking the suspected vehicle as a modified vehicle, and sending a modified vehicle signal to the official terminal unit through the big data platform; analyzing the vehicle image of the suspected vehicle and the vehicle image of the primarily selected vehicle, and judging whether to send the vehicle owner information to the official terminal unit according to the vehicle image analysis result; the comparison analysis module analyzes the vehicle characteristic information in sequence, and generates a corresponding analysis result in each step in the analysis process, so that the service terminal module can obtain an accurate comparison result;
3. the invention is provided with a passerby reward module which sends reward signals to an intelligent terminal of a passerby, and the passerby searches vehicles through suspicion information; before the reward signal is sent to the intelligent terminal of the passerby, the passerby needs to be registered through the intelligent terminal, and the passerby logs in after the registration is successful; receiving suspicion information through an intelligent terminal after the passerby successfully logs in; when a passerby encounters a vehicle with the same vehicle type, vehicle image and license plate number as those in the suspicion information, acquiring encountered vehicle detail information and sending the encountered vehicle detail information to a passerby appreciation module; the passerby appreciation module compares and matches the detailed images of the vehicles with the images of the vehicles in the suspicion information; when the comparison and matching results are consistent, the vehicle detail information is sent to a big data platform, and meanwhile, points are rewarded for passers-by; otherwise, sending an information inconsistency signal to the intelligent terminal of the passerby through the passerby appreciation module; the passerby reward module fully exerts the advantages of big data and is beneficial to improving the search rate of vehicles.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the common principles of the present invention;
fig. 2 is a schematic view of the private principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1, an automobile comparison and analysis system based on big data comprises a big data platform, a data storage module, a comparison and analysis module, a business terminal unit and a road reward module;
the big data platform is respectively in linear connection with the data storage module and the comparative analysis module; the big data platform is respectively in communication connection with the official terminal unit and the passerby appreciation module; the comparison analysis module is wirelessly connected with the official terminal unit;
the official terminal unit is used for sending vehicle characteristic information of a suspected vehicle to the big data platform through the intelligent terminal by the official user; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and a tablet computer;
the big data platform performs primary screening according to the vehicle characteristic information of the suspected vehicle to obtain a primary vehicle, and sends the primary vehicle to the comparison analysis module;
the passerby reward module is used for searching vehicles by passerby through reward signals and first suspicion information; the reward signal and the suspicion information are obtained through a big data platform;
and the comparison analysis module is used for analyzing the primarily selected vehicle to obtain an analysis result and respectively feeding the analysis result back to the official terminal unit and the big data platform.
Further, the official business terminal unit is used for sending the vehicle characteristic information of suspect vehicle to big data platform, and big data platform obtains the vehicle of primary election according to the vehicle characteristic information of suspect vehicle, includes:
the official personnel carry out identity authentication through the intelligent terminal;
after the official terminal unit receives the identity verification success signal, vehicle characteristic information of the suspected vehicle is sent to the big data platform through the intelligent terminal; the vehicle characteristic information of the suspected vehicle comprises a vehicle type, a characteristic number, a vehicle image, a license plate number and vehicle owner information; the characteristic number comprises an engine number and a frame number; vehicle types include passenger cars and cargo cars; the vehicle owner information comprises a vehicle owner name, a vehicle owner address and a mobile phone number for real-name authentication of the vehicle owner;
after receiving the vehicle characteristic information of the suspected vehicle, the big data platform acquires a characteristic information comparison library through the data storage module;
searching in the characteristic information comparison library by taking the frame number of the suspected vehicle as a search keyword, and marking the corresponding vehicle in the characteristic information comparison library as a primary vehicle when the vehicle corresponding to the frame number is searched; when the vehicle corresponding to the frame number is not searched, a data missing signal is sent to a business terminal unit through a big data platform;
the method comprises the steps that a primary vehicle is sent to a comparison analysis module through a big data platform, and meanwhile an appreciation signal and suspicion information are sent to a passerby appreciation module; the suspect information is vehicle characteristic information.
Further, the comparative analysis module is used for analyzing the primary vehicle, and comprises:
after the comparison analysis module receives the primary vehicle, comparing the engine number of the suspect vehicle with the engine number corresponding to the primary vehicle, and performing the next step when the comparison results are consistent; when the comparison results are inconsistent, the suspect vehicle is marked as an assembled vehicle, and a vehicle assembling signal is sent to the official terminal unit through the big data platform;
matching the vehicle types and license plate numbers of the suspected vehicle and the initially selected vehicle, and performing the next step when the matching results are consistent; when at least one matching result is inconsistent, marking the suspected vehicle as a modified vehicle, and sending a modified vehicle signal to the official terminal unit through the big data platform;
the method comprises the following steps of analyzing a vehicle image of a suspected vehicle and a vehicle image of a vehicle selected primarily, wherein the specific analysis steps comprise:
preprocessing the image of the suspected vehicle to obtain a first vehicle image, and preprocessing the image of the primarily selected vehicle to obtain a second vehicle image; the image preprocessing comprises image cutting, image enhancement, image denoising and gray level transformation;
acquiring pixel value curves of a first vehicle image and a second vehicle image, and respectively marking the pixel value curves as a first pixel value curve and a second pixel value curve;
deriving the first pixel value curve and the second pixel value curve to obtain the number of inflection points of the first pixel value curve and the number of inflection points of the second pixel value curve; when the number of the first pixel value curve inflection points is not equal to that of the second pixel value curve inflection points, the suspect vehicle is marked as a color-changing vehicle; when the number of the inflection points of the first pixel value curve is equal to that of the inflection points of the second pixel value curve, marking the inflection points as
Figure 762714DEST_PATH_IMAGE014
，
Figure DEST_PATH_IMAGE055
=1，2，…，
Figure DEST_PATH_IMAGE056
；
Obtaining a first pixel value curve inflection point pixel value sequence
Figure DEST_PATH_IMAGE057
、
Figure DEST_PATH_IMAGE058
、…、
Figure DEST_PATH_IMAGE059
Wherein
Figure DEST_PATH_IMAGE060
Is a point of inflection
Figure DEST_PATH_IMAGE061
Corresponding pixel values in the first pixel value curve; obtaining a second pixel value curve inflection point pixel value sequence
Figure DEST_PATH_IMAGE062
、
Figure 433472DEST_PATH_IMAGE018
、…、
Figure 979991DEST_PATH_IMAGE020
Wherein
Figure 340434DEST_PATH_IMAGE022
Is a point of inflection
Figure 554378DEST_PATH_IMAGE023
A corresponding pixel value in the second pixel value curve;
by the formula
Figure 210749DEST_PATH_IMAGE025
Obtaining an inflection point pixel value evaluation coefficient
Figure DEST_PATH_IMAGE063
(ii) a Wherein
Figure 431515DEST_PATH_IMAGE029
And
Figure DEST_PATH_IMAGE064
is a predetermined scale factor, and
Figure 772147DEST_PATH_IMAGE029
and
Figure DEST_PATH_IMAGE065
all are real numbers greater than 0;
when-inflection-point pixel value evaluation coefficient
Figure DEST_PATH_IMAGE066
Satisfy the requirement of
Figure DEST_PATH_IMAGE067
When the vehicle image matching result is consistent, the vehicle image matching result of the suspect vehicle and the vehicle image matching result of the primary vehicle are judged, and the vehicle owner information corresponding to the suspect vehicle is sent to the official terminal unit through the big data platform; when-inflection-point pixel value evaluation coefficient
Figure DEST_PATH_IMAGE068
Satisfy the requirement of
Figure DEST_PATH_IMAGE069
If so, judging that the matching result of the vehicle image of the suspected vehicle is inconsistent with the matching result of the vehicle image of the initially selected vehicle, marking the suspected vehicle as a color-changing vehicle, and sending a vehicle color-changing signal to a business terminal unit through a big data platform; wherein
Figure DEST_PATH_IMAGE070
Evaluating a coefficient threshold for a preset inflection point pixel value;
and sending the vehicle owner information sending record to a data storage module for storage.
Further, passerby rewards gold module will reward gold signal transmission to passerby's intelligent terminal, and passerby seeks the vehicle through first suspect information, includes:
before the reward signal is sent to the intelligent terminal of the passerby, the passerby needs to be registered through the intelligent terminal, and the passerby logs in after the registration is successful;
receiving first suspicion information through an intelligent terminal after a passerby successfully logs in;
when the passerby encounters a vehicle with the same vehicle type, vehicle image and license plate number as those in the first suspicion information, acquiring encountered vehicle detail information and sending the encountered vehicle detail information to a passerby appreciation module; the vehicle detail information comprises a detail image of the vehicle and a vehicle position;
the passerby appreciation module compares and matches the detailed image of the vehicle with the vehicle image in the first suspicion information; when the comparison and matching results are consistent, the vehicle detail information is sent to a big data platform, and meanwhile, points are rewarded for passers-by; otherwise, the information inconsistency signal is sent to the intelligent terminal of the passerby through the passerby appreciation module.
Further, the passerby registration includes:
the passerby sends passerby registration information to the passerby appreciation module through the intelligent terminal; the passerby registration information comprises a name, an electronic photo of an identity card and a mobile phone number authenticated by a real name;
the passerby reward module sends passerby registration information to the big data platform, the big data platform analyzes crime records of passerby through the passerby registration information, when the passerby crime records, a passerby account and a password are generated according to the passerby registration information, and the passerby account and the password are sent to an intelligent terminal of the passerby through the passerby reward module;
when the passerby has a crime record, adding the passerby to a registration blacklist, and sending the registration blacklist to a data storage module for storage; the passers-by in the registration blacklist are not allowed to be registered.
Further, the obtaining step of the first pixel value curve includes:
acquiring pixel points of a first vehicle image and pixel values of corresponding pixel points, and performing descending order arrangement on the pixel values to acquire a pixel value arrangement table; the pixel value arrangement table comprises pixel values and pixel points corresponding to the pixels;
obtaining a pixel point sequence through a pixel value arrangement table, wherein the pixel point sequence is
Figure DEST_PATH_IMAGE071
、
Figure DEST_PATH_IMAGE072
、…、
Figure DEST_PATH_IMAGE073
(ii) a Wherein
Figure DEST_PATH_IMAGE074
The serial number of the pixel point is the serial number,
Figure DEST_PATH_IMAGE075
is a pixel point
Figure DEST_PATH_IMAGE076
Corresponding pixel value, and
Figure DEST_PATH_IMAGE077
；
and fitting a curve according to the pixel point sequence and marking the fitted curve as a first pixel value curve.
Further, the specific steps of identity authentication include:
the official personnel input verification information through the intelligent terminal and send the verification information to the big data platform; the verification information comprises name, job number, work unit and face image information;
after receiving the verification information, the big data platform acquires an authority query table through the data storage module; the authority inquiry table is preset personnel information with access authority, and comprises names, work numbers, working units and face image information of the personnel information with the access authority;
matching the verification information with the authority inquiry table to obtain an identity matching result; the identity matching result comprises a name matching result, a job number matching result, a work unit matching result and face image similarity;
respectively marking the name matching result, the job number matching result, the work unit matching result and the face image similarity as
Figure DEST_PATH_IMAGE079
、
Figure DEST_PATH_IMAGE081
、
Figure DEST_PATH_IMAGE083
And
Figure DEST_PATH_IMAGE085
(ii) a When the values of the name matching result, the job number matching result and the work unit matching result are 0, the matching is failed, and when the values of the name matching result, the job number matching result and the work unit matching result are 1, the matching is successful;
by the formula
Figure DEST_PATH_IMAGE087
Obtaining identity verification coefficient
Figure DEST_PATH_IMAGE089
(ii) a Wherein
Figure DEST_PATH_IMAGE091
To preset a proportionality coefficientAnd is and
Figure DEST_PATH_IMAGE092
a real number greater than 0;
coefficient of identity verification
Figure DEST_PATH_IMAGE093
Satisfy the requirement of
Figure DEST_PATH_IMAGE095
If so, judging that the authentication is successful, and sending an authentication success signal to the official terminal unit through the big data platform; coefficient of identity verification
Figure DEST_PATH_IMAGE096
Satisfy the requirement of
Figure DEST_PATH_IMAGE098
If so, judging that the authentication fails, and sending an authentication failure signal to the official terminal unit through the big data platform; wherein
Figure DEST_PATH_IMAGE100
Is a preset identity authentication coefficient threshold value;
after receiving the identity authentication failure signal, the official terminal reminds the official personnel to perform identity authentication again;
and sending the verification information, the identity verification coefficient and the identity verification success signal sending record to the data storage module for storage.
Further, the feature information comparison library is obtained through a third-party platform and is screened, and the method comprises the following steps:
obtaining standard vehicle characteristic information through a third-party platform; the third-party platform comprises a vehicle management station and a vehicle manufacturer; the standard vehicle characteristic information comprises a vehicle type, a vehicle image and a characteristic number;
screening the standard vehicle characteristic information on the basis of the characteristic numbers, and sending the repeated characteristic numbers to a business terminal unit for manual verification when the repeated characteristic numbers appear in the standard characteristic information;
after the official personnel perform manual verification, the standard vehicle information is updated to generate a characteristic information comparison library; the characteristic information comparison base is updated regularly and stored in the data storage module.
Example two:
referring to fig. 2, an automobile comparative analysis system based on big data includes a big data platform, a data storage module, a comparative analysis module, a private terminal unit and a passerby appreciation module;
the private terminal unit is used for sending the vehicle characteristic information of the own vehicle of the user to the big data platform through the intelligent terminal, and the big data platform acquires the primary vehicle according to the vehicle characteristic information of the own vehicle, wherein the primary vehicle comprises the vehicle characteristic information;
the user performs identity authentication through the intelligent terminal;
after receiving the identity verification success signal, the private terminal unit sends the vehicle characteristic information of the free vehicle to the big data platform through the intelligent terminal; the vehicle characteristic information of the own vehicle of the user comprises a vehicle type, a characteristic number, a vehicle image and a license plate number;
and after receiving the vehicle characteristic information of the own vehicle, the big data platform sends an appreciation signal and second suspect information to the passerby appreciation module, wherein the second suspect information is the vehicle characteristic information of the own vehicle.
The above formulas are all calculated by removing dimensions and taking values thereof, the formula is one closest to the real situation obtained by collecting a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
taking the service terminal unit in the service terminal module as an example:
the official personnel carry out identity authentication through the intelligent terminal; after the official terminal unit receives the identity verification success signal, vehicle characteristic information of the suspected vehicle is sent to the big data platform through the intelligent terminal; after receiving the vehicle characteristic information of the suspected vehicle, the big data platform acquires a characteristic information comparison library through the data storage module; searching in the characteristic information comparison library by taking the frame number of the suspected vehicle as a search keyword, and marking the corresponding vehicle in the characteristic information comparison library as a primary vehicle when the vehicle corresponding to the frame number is searched; when the vehicle corresponding to the frame number is not searched, a data missing signal is sent to a business terminal unit through a big data platform; the method comprises the steps that a primary vehicle is sent to a comparison analysis module through a big data platform, and meanwhile an appreciation signal and first suspicion information are sent to a passerby appreciation module;
after the comparison analysis module receives the primary vehicle, comparing the engine number of the suspect vehicle with the engine number corresponding to the primary vehicle, and performing the next step when the comparison results are consistent; when the comparison results are inconsistent, the suspect vehicle is marked as an assembled vehicle, and a vehicle assembling signal is sent to the official terminal unit through the big data platform; matching the vehicle types and license plate numbers of the suspected vehicle and the initially selected vehicle, and performing the next step when the matching results are consistent; when at least one matching result is inconsistent, marking the suspected vehicle as a modified vehicle, and sending a modified vehicle signal to the official terminal unit through the big data platform; analyzing the vehicle image of the suspected vehicle and the vehicle image of the primarily selected vehicle, and judging whether to send the vehicle owner information to the official terminal unit according to the vehicle image analysis result;
before the reward signal is sent to the intelligent terminal of the passerby, the passerby needs to be registered through the intelligent terminal, and the passerby logs in after the registration is successful; receiving suspicion information through an intelligent terminal after the passerby successfully logs in; when a passerby encounters a vehicle with the same vehicle type, vehicle image and license plate number as those in the suspicion information, acquiring encountered vehicle detail information and sending the encountered vehicle detail information to a passerby appreciation module; the passerby appreciation module compares and matches the detailed images of the vehicles with the images of the vehicles in the suspicion information; when the comparison and matching results are consistent, the vehicle detail information is sent to a big data platform, and meanwhile, points are rewarded for passers-by; otherwise, the information inconsistency signal is sent to the intelligent terminal of the passerby through the passerby appreciation module.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (6)

1. A big data based automobile comparison and analysis system is characterized by comprising a big data platform, a data storage module, a comparison and analysis module, a service terminal module and a passerby appreciation module;
the big data platform is respectively in linear connection with the data storage module and the comparative analysis module; the big data platform is respectively in communication connection with the service terminal module and the passerby appreciation module; the comparison analysis module is wirelessly connected with the service terminal module; the service terminal module comprises a public service terminal unit and a private terminal unit;
the service terminal module is used for sending vehicle characteristic information to the big data platform through the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and a tablet computer; the vehicle characteristic information comprises vehicle characteristic information of a suspected vehicle and vehicle characteristic information of a free vehicle;
the big data platform performs primary screening according to the vehicle characteristic information to obtain a primary vehicle, and sends the primary vehicle to the comparison and analysis module;
the passerby appreciation module is used for searching vehicles by passerby through appreciation signals and suspicion information; the reward signal and the suspicion information are obtained through a big data platform; the suspicion information comprises first suspicion information and second suspicion information;
and the comparison analysis module is used for analyzing the primarily selected vehicle to obtain an analysis result and feeding the analysis result back to the service terminal module and the big data platform respectively.
2. The big-data-based automobile comparative analysis system according to claim 1, wherein the business terminal unit is configured to send vehicle characteristic information of a suspected vehicle to the big data platform, and the big data platform obtains a vehicle for primary selection according to the vehicle characteristic information of the suspected vehicle, and the big data platform includes:
the official personnel carry out identity authentication through the intelligent terminal;
after the official terminal unit receives the identity verification success signal, vehicle characteristic information of the suspected vehicle is sent to the big data platform through the intelligent terminal; the vehicle characteristic information of the suspected vehicle comprises a vehicle type, a characteristic number, a vehicle image, a license plate number and vehicle owner information; the characteristic numbers comprise engine numbers and frame numbers; the vehicle types include passenger cars and cargo cars; the vehicle owner information comprises a vehicle owner name, a vehicle owner address and a mobile phone number for real-name authentication of the vehicle owner;
after receiving the vehicle characteristic information of the suspected vehicle, the big data platform acquires a characteristic information comparison library through the data storage module;
searching in the characteristic information comparison library by taking the frame number of the suspected vehicle as a search keyword, and marking the corresponding vehicle in the characteristic information comparison library as a primary vehicle when the vehicle corresponding to the frame number is searched; when the vehicle corresponding to the frame number is not searched, a data missing signal is sent to a business terminal unit through a big data platform;
the method comprises the steps that a primary vehicle is sent to a comparison analysis module through a big data platform, and meanwhile an appreciation signal and first suspicion information are sent to a passerby appreciation module; the first suspected information is vehicle characteristic information of a suspected vehicle.
3. The big-data-based automobile comparative analysis system according to claim 1, wherein the private terminal unit is used for the user to send the vehicle characteristic information of the user's own vehicle to the big data platform through the intelligent terminal, and the big data platform obtains the vehicle to be selected initially according to the vehicle characteristic information of the own vehicle, including;
the user performs identity authentication through the intelligent terminal;
after receiving the identity verification success signal, the private terminal unit sends the vehicle characteristic information of the free vehicle to the big data platform through the intelligent terminal; the vehicle characteristic information of the own vehicle of the user comprises a vehicle type, a characteristic number, a vehicle image and a license plate number;
and after receiving the vehicle characteristic information of the own vehicle, the big data platform sends an appreciation signal and second suspect information to the passerby appreciation module, wherein the second suspect information is the vehicle characteristic information of the free vehicle.
4. The big data-based automobile comparative analysis system according to claim 1, wherein the comparative analysis module is used for analyzing the primary vehicle, and comprises:
after the comparison analysis module receives the primary vehicle, comparing the engine number of the suspect vehicle with the engine number corresponding to the primary vehicle, and performing the next step when the comparison results are consistent; when the comparison results are inconsistent, the suspect vehicle is marked as an assembled vehicle, and a vehicle assembling signal is sent to the official terminal unit through the big data platform;
matching the vehicle types and license plate numbers of the suspected vehicle and the initially selected vehicle, and performing the next step when the matching results are consistent; when at least one matching result is inconsistent, marking the suspected vehicle as a modified vehicle, and sending a modified vehicle signal to the official terminal unit through the big data platform;
the method comprises the following steps of analyzing a vehicle image of a suspected vehicle and a vehicle image of a vehicle selected primarily, wherein the specific analysis steps comprise:
preprocessing the image of the suspected vehicle to obtain a first vehicle image, and preprocessing the image of the primarily selected vehicle to obtain a second vehicle image;
acquiring pixel value curves of a first vehicle image and a second vehicle image, and respectively marking the pixel value curves as a first pixel value curve and a second pixel value curve;
deriving the first pixel value curve and the second pixel value curve to obtain the number of inflection points of the first pixel value curve and the number of inflection points of the second pixel value curve; when the number of the first pixel value curve inflection points is not equal to that of the second pixel value curve inflection points, the suspect vehicle is marked as a color-changing vehicle; when the number of the inflection points of the first pixel value curve is equal to that of the inflection points of the second pixel value curve, marking the inflection points as
Figure 464804DEST_PATH_IMAGE001
，
Figure 462716DEST_PATH_IMAGE002
=1，2，…，
Figure 593614DEST_PATH_IMAGE003
；
Obtaining a first pixel value curve inflection point pixel value sequence
Figure 453117DEST_PATH_IMAGE004
、
Figure 500707DEST_PATH_IMAGE005
、…、
Figure 355006DEST_PATH_IMAGE006
Wherein
Figure 284785DEST_PATH_IMAGE007
Is a point of inflection
Figure 151241DEST_PATH_IMAGE008
Corresponding pixel values in the first pixel value curve; obtaining a second pixel value curve inflection point pixel value sequence
Figure 787759DEST_PATH_IMAGE009
、
Figure 878206DEST_PATH_IMAGE010
、…、
Figure 718117DEST_PATH_IMAGE011
Wherein
Figure 719090DEST_PATH_IMAGE012
Is a point of inflection
Figure 475693DEST_PATH_IMAGE013
A corresponding pixel value in the second pixel value curve;
by the formula
Figure 940303DEST_PATH_IMAGE014
Obtaining an inflection point pixel value evaluation coefficient
Figure 782358DEST_PATH_IMAGE015
(ii) a Wherein
Figure 787354DEST_PATH_IMAGE016
And
Figure 132884DEST_PATH_IMAGE017
is a predetermined scale factor, and
Figure 33975DEST_PATH_IMAGE016
and
Figure 363326DEST_PATH_IMAGE018
all are real numbers greater than 0;
when-inflection-point pixel value evaluation coefficient
Figure 903504DEST_PATH_IMAGE019
Satisfy the requirement of
Figure 119853DEST_PATH_IMAGE020
When the vehicle image matching result is consistent, the vehicle image matching result of the suspect vehicle and the vehicle image matching result of the primary vehicle are judged, and the vehicle owner information corresponding to the suspect vehicle is sent to the official terminal unit through the big data platform; when-inflection-point pixel value evaluation coefficient
Figure 237850DEST_PATH_IMAGE021
Satisfy the requirement of
Figure 539650DEST_PATH_IMAGE022
If so, judging that the matching result of the vehicle image of the suspected vehicle is inconsistent with the matching result of the vehicle image of the initially selected vehicle, marking the suspected vehicle as a color-changing vehicle, and sending a vehicle color-changing signal to a business terminal unit through a big data platform; wherein
Figure 886449DEST_PATH_IMAGE023
Evaluating a coefficient threshold for a preset inflection point pixel value;
and sending the vehicle owner information sending record to a data storage module for storage.
5. The automobile comparison and analysis system based on big data according to claim 1, wherein the passerby reward module sends reward signals to an intelligent terminal of a passerby, and the passerby searches vehicles through suspicion information, and the system comprises:
before the reward signal is sent to the intelligent terminal of the passerby, the passerby needs to be registered through the intelligent terminal, and the passerby logs in after the registration is successful;
receiving suspicion information through an intelligent terminal after the passerby successfully logs in;
when a passerby encounters a vehicle with the same vehicle type, vehicle image and license plate number as those in the suspicion information, acquiring encountered vehicle detail information and sending the encountered vehicle detail information to a passerby appreciation module; the vehicle detail information comprises a detail image and a vehicle position of the vehicle;
the passerby appreciation module compares and matches the detailed images of the vehicles with the images of the vehicles in the suspicion information; when the comparison and matching results are consistent, the vehicle detail information is sent to a big data platform, and meanwhile, points are rewarded for passers-by; otherwise, the information inconsistency signal is sent to the intelligent terminal of the passerby through the passerby appreciation module.
6. The big-data-based automobile contrast analysis system according to claim 4, wherein the obtaining step of the first pixel value curve comprises:
acquiring pixel points of a first vehicle image and pixel values of corresponding pixel points, and performing descending order arrangement on the pixel values to acquire a pixel value arrangement table; the pixel value arrangement table comprises pixel values and pixel points corresponding to the pixels;
obtaining a pixel point sequence through a pixel value arrangement table, wherein the pixel point sequence is
Figure 472151DEST_PATH_IMAGE024
、
Figure 729693DEST_PATH_IMAGE025
、…、
Figure 502477DEST_PATH_IMAGE026
(ii) a Wherein
Figure DEST_PATH_IMAGE027
The serial number of the pixel point is the serial number,
Figure 715283DEST_PATH_IMAGE028
is a pixel point
Figure DEST_PATH_IMAGE029
Corresponding pixel value, and
Figure 968541DEST_PATH_IMAGE030
；
and fitting a curve according to the pixel point sequence and marking the fitted curve as a first pixel value curve.
CN202011461510.0A 2020-12-14 2020-12-14 Big data-based automobile comparison analysis system Active CN112231509B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011461510.0A CN112231509B (en) 2020-12-14 2020-12-14 Big data-based automobile comparison analysis system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011461510.0A CN112231509B (en) 2020-12-14 2020-12-14 Big data-based automobile comparison analysis system

Publications (2)

Publication Number Publication Date
CN112231509A true CN112231509A (en) 2021-01-15
CN112231509B CN112231509B (en) 2021-05-25

Family

ID=74124635

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011461510.0A Active CN112231509B (en) 2020-12-14 2020-12-14 Big data-based automobile comparison analysis system

Country Status (1)

Country Link
CN (1) CN112231509B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108765952A (en) * 2018-06-12 2018-11-06 西安银江智慧城市技术有限公司 A kind of traffic big data suspected vehicles raid supervision method and device
CN111354473A (en) * 2020-03-20 2020-06-30 天津绿州能源装备有限公司 Application system for searching new coronary pneumonia infected person based on navigation map and mobile phone positioning
CN112016520A (en) * 2020-09-15 2020-12-01 平安国际智慧城市科技股份有限公司 AI-based traffic violation voucher generation method, device, terminal and storage medium

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108765952A (en) * 2018-06-12 2018-11-06 西安银江智慧城市技术有限公司 A kind of traffic big data suspected vehicles raid supervision method and device
CN111354473A (en) * 2020-03-20 2020-06-30 天津绿州能源装备有限公司 Application system for searching new coronary pneumonia infected person based on navigation map and mobile phone positioning
CN112016520A (en) * 2020-09-15 2020-12-01 平安国际智慧城市科技股份有限公司 AI-based traffic violation voucher generation method, device, terminal and storage medium

Also Published As

Publication number Publication date
CN112231509B (en) 2021-05-25

Similar Documents

Publication Publication Date Title
CN109920174B (en) Book borrowing method and device, electronic equipment and storage medium
CN109344271A (en) Video portrait records handling method and its system
CN110837807A (en) Identification method and system for fake-licensed vehicle
CN101449280A (en) A validation engine
CN107273833A (en) Method and its system for monitoring floating population
CN113536261A (en) Authority management method, device, computer device and storage medium
CN113971821A (en) Driver information determination method and device, terminal device and storage medium
CN111274283A (en) Track display method and device
CN116321101A (en) Smart community information sharing method and system
CN112231509B (en) Big data-based automobile comparison analysis system
CN111898548B (en) Visitor identity authentication method and device based on machine learning and computer equipment
CN113065016A (en) Offline store information processing method, device, equipment and system
CN112527861A (en) Personnel portrait analysis method based on big data real-time analysis
CN112581337B (en) Method and terminal for judging whether real population is bounced or not
CN111475657B (en) Display equipment, display system and entity alignment method
CN113297488A (en) Data processing method and system based on big data and artificial intelligence
CN114491049A (en) Office system asset allocation method based on information management
CN112333182A (en) File processing method, device, server and storage medium
CN114999166A (en) Vehicle identification method and device, electronic equipment and computer readable storage medium
CN111064796B (en) Method and device for analyzing adjoint relationship and method for training analysis model
CN111026954A (en) Vehicle information recommendation method and device
Garima et al. Crime analysis in Chicago city
KR102650518B1 (en) System for providing resident checking service using car number
Bellman et al. A classification approach to finding buildings in large scale aerial photographs
CN112597991B (en) Data processing method, data processing device, server and client

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
TR01 Transfer of patent right

Effective date of registration: 20221212

Address after: 610, Floor 6, Block A, No. 2, Lize Middle Second Road, Chaoyang District, Beijing 100102

Patentee after: Zhongguancun Technology Leasing Co.,Ltd.

Address before: 310000 South 1st floor, building 5, 1288 liangmu Road, Cangqian street, Yuhang District, Hangzhou City, Zhejiang Province

Patentee before: HANGZHOU YIJIQINGCHEN INFORMATION TECHNOLOGY Co.,Ltd.

TR01 Transfer of patent right