CN113990084A - Big data analysis method and system applied to traffic management - Google Patents
Big data analysis method and system applied to traffic management Download PDFInfo
- Publication number
- CN113990084A CN113990084A CN202111504427.1A CN202111504427A CN113990084A CN 113990084 A CN113990084 A CN 113990084A CN 202111504427 A CN202111504427 A CN 202111504427A CN 113990084 A CN113990084 A CN 113990084A
- Authority
- CN
- China
- Prior art keywords
- target
- vehicle
- target vehicle
- traffic
- driving
- 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.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000007405 data analysis Methods 0.000 title claims abstract description 22
- 230000002159 abnormal effect Effects 0.000 claims abstract description 36
- 238000012545 processing Methods 0.000 claims abstract description 17
- 231100000279 safety data Toxicity 0.000 claims abstract description 12
- 238000012544 monitoring process Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 230000004044 response Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 239000000835 fiber Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Health & Medical Sciences (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The application relates to a big data analysis method and a big data analysis system applied to traffic management, which are used for determining current traffic management items acquired by a traffic management end in a target traffic supervision area; analyzing and processing the traffic safety data to obtain a target vehicle and the current running state of the target vehicle in the current traffic management items; according to the adjustment of the current running state of the target vehicle and the statistical conditions set for the target traffic supervision area, determining the abnormal running condition of the target vehicle in the statistical conditions, and according to the abnormal running condition of the target vehicle, determining the vehicle running speed in the target traffic supervision area. Therefore, the vehicle running speed in the target traffic supervision area can be comprehensively counted, and meanwhile, the problem of traffic jam in the target traffic supervision area can be judged through the vehicle running speed, so that the traffic management efficiency and the service quality can be improved.
Description
Technical Field
The present application relates to the field of traffic management and big data technology, and in particular, to a big data analysis method and system applied to traffic management.
Background
At present, the continuous progress of scientific technology promotes the traffic management to be upgraded and transformed towards intellectualization. However, in practical applications, how to analyze traffic management with high quality is a technical problem that needs to be improved at present.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a big data analysis method and a big data analysis system applied to traffic management.
The application provides a big data analysis method applied to traffic management, which comprises the following steps: determining current traffic management items acquired by a traffic management end in a target traffic supervision area; analyzing and processing traffic safety data of the current traffic management items to obtain target vehicles in the current traffic management items and current running states of the target vehicles; determining abnormal driving conditions of the target vehicle in the statistical conditions through adjustment of the current driving state of the target vehicle and the statistical conditions set for the target traffic supervision area, wherein the abnormal driving conditions comprise accelerated driving into the target traffic supervision area or overspeed driving into the target traffic supervision area; and determining the vehicle running speed in the target traffic monitoring area according to the abnormal running condition of the target vehicle.
In a possible implementation manner, the statistical condition includes a first condition and a second condition that are set simultaneously, and the determining, by the adjustment of the current driving state of the target vehicle and the statistical condition set for the target traffic supervision area, an abnormal driving situation of the target vehicle occurring within the statistical condition includes: determining that the target vehicle accelerates into the target traffic supervision area in response to the current driving state of the target vehicle being adjusted from a first condition to a second condition; or, in response to the current driving state of the target vehicle being adjusted from the second condition to the first condition, determining that the target vehicle is speeding into the target traffic supervision area.
In one possible implementation, the determining the vehicle running speed in the target traffic monitoring area through the abnormal running condition of the target vehicle includes: on the premise that the abnormal driving condition represents that the target vehicle is accelerated to enter the target traffic supervision area or the target vehicle is overspeed to enter the target traffic supervision area, testing whether a label corresponding to the target vehicle is recorded in an associated database, wherein the label is used for distinguishing different analyzed target vehicles; on the premise that the label corresponding to the target vehicle is not recorded in the association database, optimizing the vehicle running speed corresponding to the target traffic supervision area, and adding the label corresponding to the target vehicle to the association database to represent that the target vehicle has abnormal running condition; or, on the premise that the label corresponding to the target vehicle is recorded in the association database, the running speed of the vehicle corresponding to the target traffic supervision area is not optimized.
In a possible implementation manner, the vehicle running speed includes a maximum speed and a minimum speed, and on the premise that no tag corresponding to the target vehicle is recorded in the association database, the optimizing the vehicle running speed corresponding to the target traffic supervision area includes: optimizing the maximum speed corresponding to the target traffic supervision area on the premise that the abnormal driving condition represents that the target vehicle accelerates to enter the target traffic supervision area and no label corresponding to the target vehicle is recorded in the association database; or, on the premise that the abnormal driving condition represents that the target vehicle drives into the target traffic supervision area at an overspeed and no label corresponding to the target vehicle is recorded in the association database, optimizing the minimum speed corresponding to the target traffic supervision area; and determining the vehicle running speed corresponding to the target traffic supervision area based on the maximum speed and the minimum speed.
In a possible implementation manner, analyzing and processing traffic safety data of the current traffic management item to obtain a target vehicle in the current traffic management item and a current driving state of the target vehicle, includes: and analyzing and positioning the traffic safety data of the statistical condition of the current traffic management item through a data analysis model to obtain a target vehicle of the current traffic management item in the statistical condition and the current running state of the target vehicle.
In one possible implementation manner, the determining the vehicle running speed in the target traffic supervision area according to the abnormal running condition of the target vehicle includes: determining an accelerated driving statistical result of a target vehicle accelerated to drive into the target traffic supervision area according to the maximum speed corresponding to each driving route; determining the overspeed driving statistical result of the target vehicle which enters the target traffic supervision area at overspeed according to the respective corresponding minimum speed of each driving route; and determining the vehicle running speed in the target traffic monitoring area according to the accelerated driving statistical result and the overspeed driving statistical result of each running route.
The embodiment of the application also provides a traffic management big data analysis system, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the application, the traffic safety data is analyzed and processed on the determined current traffic management items to determine the current running states of the target vehicle and determine the abnormal running condition of the target vehicle in the statistical condition, so that the running speed of the vehicle in the target traffic supervision area can be comprehensively counted on the basis, meanwhile, the problem of traffic jam in the target traffic supervision area can be judged through the running speed of the vehicle, and the traffic management efficiency and the service quality can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a big data analysis method applied to traffic management according to an embodiment of the present application.
Fig. 2 is a schematic hardware structure diagram of a traffic management big data analysis system according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a schematic flow chart of a big data analysis method applied to traffic management, and the method may specifically include the following technical solutions recorded in steps 11 to 13.
In an exemplary embodiment, the analyzing and processing the traffic safety data of the current traffic management item to obtain the target vehicle in the current traffic management item and the current driving state of the target vehicle may specifically include: and analyzing and positioning the traffic safety data of the statistical condition of the current traffic management item through a data analysis model to obtain a target vehicle of the current traffic management item in the statistical condition and the current running state of the target vehicle.
And step 12, determining abnormal running conditions of the target vehicle in the statistical conditions through adjustment of the current running state of the target vehicle and the statistical conditions set for the target traffic supervision area.
In the embodiment of the application, the abnormal driving condition includes accelerating to enter the target traffic supervision area or speeding to enter the target traffic supervision area.
In an exemplary embodiment, the statistical condition includes a first condition and a second condition set simultaneously. Based on this, the determining of the abnormal driving condition of the target vehicle occurring in the statistical condition through the adjustment of the current driving state of the target vehicle and the statistical condition set for the target traffic supervision area may specifically include the following: determining that the target vehicle accelerates into the target traffic supervision area in response to the current driving state of the target vehicle being adjusted from a first condition to a second condition; or, in response to the current driving state of the target vehicle being adjusted from the second condition to the first condition, determining that the target vehicle is speeding into the target traffic supervision area.
And step 13, determining the vehicle running speed in the target traffic monitoring area according to the abnormal running condition of the target vehicle.
In an exemplary embodiment, the determining the vehicle driving speed in the target traffic monitoring area according to the abnormal driving condition of the target vehicle may specifically include: on the premise that the abnormal driving condition represents that the target vehicle is accelerated to enter the target traffic supervision area or the target vehicle is overspeed to enter the target traffic supervision area, testing whether a label corresponding to the target vehicle is recorded in an associated database, wherein the label is used for distinguishing different analyzed target vehicles; on the premise that the label corresponding to the target vehicle is not recorded in the association database, optimizing the vehicle running speed corresponding to the target traffic supervision area, and adding the label corresponding to the target vehicle to the association database to represent that the target vehicle has abnormal running condition; or, on the premise that the label corresponding to the target vehicle is recorded in the association database, the running speed of the vehicle corresponding to the target traffic supervision area is not optimized.
In one exemplary embodiment, the vehicle travel speed includes a maximum speed and a minimum speed. Based on this, on the premise that the tag corresponding to the target vehicle is not recorded in the association database, optimizing the vehicle running speed corresponding to the target traffic supervision area may specifically include: optimizing the maximum speed corresponding to the target traffic supervision area on the premise that the abnormal driving condition represents that the target vehicle accelerates to enter the target traffic supervision area and no label corresponding to the target vehicle is recorded in the association database; or, on the premise that the abnormal driving condition represents that the target vehicle drives into the target traffic supervision area at an overspeed and no label corresponding to the target vehicle is recorded in the association database, optimizing the minimum speed corresponding to the target traffic supervision area; and determining the vehicle running speed corresponding to the target traffic supervision area based on the maximum speed and the minimum speed.
In one exemplary embodiment, the target traffic supervision area includes a plurality of driving routes, and the abnormal driving condition of each driving route corresponds to a respective driving speed of the vehicle, and the driving speed of the vehicle includes a maximum speed and a minimum speed. Based on this, the determining the vehicle running speed in the target traffic monitoring area through the abnormal running condition of the target vehicle includes: determining an accelerated driving statistical result of a target vehicle accelerated to drive into the target traffic supervision area according to the maximum speed corresponding to each driving route; determining the overspeed driving statistical result of the target vehicle which enters the target traffic supervision area at overspeed according to the respective corresponding minimum speed of each driving route; and determining the vehicle running speed in the target traffic monitoring area according to the accelerated driving statistical result and the overspeed driving statistical result of each running route.
In conclusion, the traffic safety data analysis processing is carried out on the determined current traffic management items to determine the target vehicle and the current running state of the target vehicle, and determine the abnormal running condition of the target vehicle in the statistical condition, so that the vehicle running speed in the target traffic supervision area can be comprehensively counted, meanwhile, the problem of traffic jam in the target traffic supervision area can be judged through the vehicle running speed, and the traffic management efficiency and the service quality can be improved.
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of a traffic management big data analysis system 200, which specifically includes a memory 210, a processor 220, a network module 230, and a big data analysis device. The memory 210, the processor 220, and the network module 230 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 210 stores a big data analysis device, which includes at least one software function module that can be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 executes the software program and the module stored in the memory 210.
The Memory 210 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 210 is used for storing a program, and the processor 220 executes the program after receiving an execution instruction.
The processor 220 may be an integrated circuit chip having data processing capabilities. The Processor 220 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 230 is configured to establish a communication connection between the traffic management big data analysis system 200 and other communication terminal devices through a network, so as to implement transceiving operations of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. Accordingly, it will be apparent to those skilled in the art of electronic information technology that a more direct and clear description of one embodiment is provided by describing the various operations within the embodiment. Knowing the operations to be performed, the skilled person can directly design the desired product based on considerations of said non-technical factors.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (7)
1. A big data analysis method applied to traffic management is characterized by comprising the following steps:
determining current traffic management items acquired by a traffic management end in a target traffic supervision area; analyzing and processing traffic safety data of the current traffic management items to obtain target vehicles in the current traffic management items and current running states of the target vehicles;
determining abnormal driving conditions of the target vehicle in the statistical conditions through adjustment of the current driving state of the target vehicle and the statistical conditions set for the target traffic supervision area, wherein the abnormal driving conditions comprise accelerated driving into the target traffic supervision area or overspeed driving into the target traffic supervision area;
and determining the vehicle running speed in the target traffic monitoring area according to the abnormal running condition of the target vehicle.
2. The method according to claim 1, wherein the statistical condition includes a first condition and a second condition that are set simultaneously, and the determining of the abnormal driving situation of the target vehicle occurring within the statistical condition through the adjustment of the current driving state of the target vehicle and the statistical condition set for the target traffic supervision area includes:
determining that the target vehicle accelerates into the target traffic supervision area in response to the current driving state of the target vehicle being adjusted from a first condition to a second condition; or, in response to the current driving state of the target vehicle being adjusted from the second condition to the first condition, determining that the target vehicle is speeding into the target traffic supervision area.
3. The method according to claim 1 or 2, wherein the determining the vehicle travel speed in the target traffic supervision area through the abnormal travel situation of the target vehicle comprises:
on the premise that the abnormal driving condition represents that the target vehicle is accelerated to enter the target traffic supervision area or the target vehicle is overspeed to enter the target traffic supervision area, testing whether a label corresponding to the target vehicle is recorded in an associated database, wherein the label is used for distinguishing different analyzed target vehicles;
on the premise that the label corresponding to the target vehicle is not recorded in the association database, optimizing the vehicle running speed corresponding to the target traffic supervision area, and adding the label corresponding to the target vehicle to the association database to represent that the target vehicle has abnormal running condition; or, on the premise that the label corresponding to the target vehicle is recorded in the association database, the running speed of the vehicle corresponding to the target traffic supervision area is not optimized.
4. The method of claim 3, wherein the vehicle driving speed comprises a maximum speed and a minimum speed, and on the premise that the tag corresponding to the target vehicle is not recorded in the association database, the optimizing the vehicle driving speed corresponding to the target traffic supervision area comprises:
optimizing the maximum speed corresponding to the target traffic supervision area on the premise that the abnormal driving condition represents that the target vehicle accelerates to enter the target traffic supervision area and no label corresponding to the target vehicle is recorded in the association database; or, on the premise that the abnormal driving condition represents that the target vehicle drives into the target traffic supervision area at an overspeed and no label corresponding to the target vehicle is recorded in the association database, optimizing the minimum speed corresponding to the target traffic supervision area; and determining the vehicle running speed corresponding to the target traffic supervision area based on the maximum speed and the minimum speed.
5. The method of claim 1, wherein analyzing and processing the traffic safety data of the current traffic management item to obtain the target vehicle in the current traffic management item and the current driving state of the target vehicle comprises:
and analyzing and positioning the traffic safety data of the statistical condition of the current traffic management item through a data analysis model to obtain a target vehicle of the current traffic management item in the statistical condition and the current running state of the target vehicle.
6. The method of claim 1, wherein the target traffic supervision area comprises a plurality of driving routes, each driving route having an abnormal driving condition corresponding to a respective driving speed of the vehicle, the driving speed of the vehicle comprising a maximum speed and a minimum speed, and the determining the driving speed of the vehicle in the target traffic supervision area according to the abnormal driving condition of the target vehicle comprises:
determining an accelerated driving statistical result of a target vehicle accelerated to drive into the target traffic supervision area according to the maximum speed corresponding to each driving route;
determining the overspeed driving statistical result of the target vehicle which enters the target traffic supervision area at overspeed according to the respective corresponding minimum speed of each driving route;
and determining the vehicle running speed in the target traffic monitoring area according to the accelerated driving statistical result and the overspeed driving statistical result of each running route.
7. A traffic management big data analysis system is characterized by comprising a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-6 by reading the computer program from the memory and running it.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111504427.1A CN113990084A (en) | 2021-12-10 | 2021-12-10 | Big data analysis method and system applied to traffic management |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111504427.1A CN113990084A (en) | 2021-12-10 | 2021-12-10 | Big data analysis method and system applied to traffic management |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113990084A true CN113990084A (en) | 2022-01-28 |
Family
ID=79733697
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111504427.1A Withdrawn CN113990084A (en) | 2021-12-10 | 2021-12-10 | Big data analysis method and system applied to traffic management |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113990084A (en) |
-
2021
- 2021-12-10 CN CN202111504427.1A patent/CN113990084A/en not_active Withdrawn
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114066475A (en) | Information security protection method based on cloud payment and server | |
| CN113990084A (en) | Big data analysis method and system applied to traffic management | |
| CN114090662A (en) | User information processing method and service system based on virtual reality | |
| CN114118484A (en) | Safety data analysis method and server based on automation plant | |
| CN113781275A (en) | Education resource management method and server | |
| CN114185318A (en) | Production line information processing method and system based on artificial intelligence | |
| CN111679885B (en) | Method, device, medium and electronic equipment for determining virtual machine drift | |
| CN114071066A (en) | Information processing method and server applied to big data video conference | |
| CN114418350A (en) | Vehicle scheduling processing method and server | |
| CN114189386A (en) | Data processing method and server based on Internet of things | |
| CN114051042B (en) | Internet of things interface access authentication method and system based on RESTFUL service | |
| CN114141016A (en) | Data analysis method and system applied to intelligent traffic | |
| CN114323136B (en) | Production environment monitoring method and system based on Internet of things | |
| CN113793058A (en) | Factory production line monitoring method and system | |
| CN114401310B (en) | Visual cloud service data optimization method and server | |
| CN113780878A (en) | Building construction monitoring method and server | |
| CN114399180A (en) | Data processing method and server based on machine tool production | |
| CN114418284A (en) | Construction behavior safety detection method and system based on intelligent construction site | |
| CN113793504A (en) | Intelligent parking guiding method based on big data and server | |
| CN114265881A (en) | Data processing method and server applied to intelligent community | |
| CN113766033A (en) | Data security processing method based on Internet of things and server | |
| CN114418352A (en) | Office equipment interaction information analysis method and server | |
| CN113762897A (en) | Information processing method and server based on intelligent logistics | |
| CN114418487A (en) | Logistics data monitoring and sharing method and server | |
| CN113742374A (en) | Information processing method and server applied to smart city |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220128 |
|
| WW01 | Invention patent application withdrawn after publication |