CN111599214A - Traffic intersection dynamic big data updating method and storage medium - Google Patents
Traffic intersection dynamic big data updating method and storage medium Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B5/00—Visible signalling systems, e.g. personal calling systems, remote indication of seats occupied
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09F—DISPLAYING; ADVERTISING; SIGNS; LABELS OR NAME-PLATES; SEALS
- G09F9/00—Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements
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Abstract
The invention relates to a traffic intersection dynamic big data updating method, which comprises the steps of establishing a danger grade distinguishing mechanism based on the number of electric vehicles at a traffic intersection by using a traffic intersection dynamic big data updating device, and dynamically updating and displaying danger grades, so that surrounding vehicles and pedestrians are effectively reminded, and traffic accidents are avoided. The traffic intersection dynamic big data updating method has clear logic and reliable design.
Description
Technical Field
The invention relates to the field of intelligent big data traffic, in particular to a traffic intersection dynamic big data updating method and a storage medium.
Background
The intelligent transportation system has the following two characteristics: the method aims to widely apply and serve the traffic information and improve the operation efficiency of the existing traffic facilities.
Compared with a common technical system, the overall requirement in the construction process of the intelligent traffic system is stricter. This integration is represented by:
(1) cross-industry characteristics. The construction of an intelligent traffic system relates to the field of numerous industries, and is a complex giant system project which is widely participated in the society, thereby causing the problem of complex inter-industry coordination.
(2) The technical field is characterized. The intelligent traffic system integrates achievements in various scientific fields such as traffic engineering, information engineering, control engineering, communication technology, computer technology and the like, and requires cooperation of technical personnel in various fields.
(3) Government, enterprises, scientific research units and colleges participate together, and proper role positioning and task sharing are important preconditions for effective development of the system.
(4) The intelligent transportation system is mainly supported by new generation information technologies such as mobile communication, broadband networks, RFID, sensors, cloud computing and the like, more meets the application requirements of people, and has improved credibility and becomes ubiquitous.
Disclosure of Invention
According to an aspect of the present invention, there is provided a traffic intersection dynamic big data updating method, the method including using a traffic intersection dynamic big data updating apparatus to establish a danger level discrimination mechanism based on the number of electric vehicles at a traffic intersection, and performing dynamic updating and displaying of danger levels, thereby realizing effective reminding of surrounding vehicles and pedestrians, and thus avoiding occurrence of traffic accidents, the traffic intersection dynamic big data updating apparatus including: and the LED display screen is arranged on the cross bar, above the traffic intersection, provided with the signal lamp and used for receiving and displaying the danger level of the traffic intersection in real time.
More specifically, in the traffic intersection dynamic big data updating apparatus, the apparatus further includes: and the full-color capturing equipment is arranged on a cross bar of which a signal lamp is arranged above the traffic intersection and is used for capturing directional data of the direction of the traffic intersection so as to obtain a corresponding directional captured image.
More specifically, in the traffic intersection dynamic big data updating apparatus, the apparatus further includes: the vehicle type distinguishing device is connected with the histogram equalization device and is used for respectively matching each standard contour of each electric vehicle with the histogram equalization image so as to take the area with the matching percentage exceeding the limit as an electric vehicle area and count the number of the electric vehicle areas in the histogram equalization device; the signal conversion equipment is respectively connected with the LED display screen and the carriage identification equipment and is used for determining the corresponding traffic intersection danger level based on the number of the electric vehicle areas in the histogram equalization equipment; in the signal conversion device, determining a corresponding traffic intersection hazard level based on the number of electric vehicle zones in the histogram equalization device comprises: the more the number of the electric vehicle areas in the histogram equalization equipment is, the higher the determined traffic intersection danger level is; the parameter extraction equipment is arranged on a cross bar of a signal lamp arranged above a traffic intersection, is connected with the full-color capture equipment, and is used for receiving the directional capture image and analyzing the number of bytes occupied by the pixel value of each pixel point in the directional capture image to obtain the reference number of bytes to be output; the pixel point counting equipment is used for receiving the directional captured image and counting the total number of pixel points in the directional captured image to obtain a reference total number output; and the grade identification device is respectively connected with the parameter extraction device and the pixel point counting device and is used for receiving the reference byte number and the reference total number and determining the grade of the data volume in direct proportion to the reference byte number and the reference total number based on the product of the reference byte number and the reference total number.
According to still another aspect of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored, which when executed, implements the steps of the traffic intersection dynamic big data updating method as described above.
The present invention has at least the following important points:
(1) a danger level distinguishing mechanism based on the number of electric vehicles is established at a traffic intersection, and dynamic updating and displaying are carried out, so that surrounding vehicles and pedestrians are effectively reminded, and traffic accidents are avoided to a certain extent;
(2) introducing grade identification equipment, parameter extraction equipment and pixel point counting equipment, and determining the data volume grade of the image based on the number of bytes occupied by the pixel value of each pixel point in the image and the total number of the pixel points in the image;
(3) on the basis of gamma correction processing, an edge enhancement processing mechanism with different strategies is determined to be executed on each component sub-image of the image based on different levels of image data volume, so that the orientation capability of the edge enhancement processing of the image is improved.
The traffic intersection dynamic big data updating method and the storage medium have clear logic and reliable design. Because the danger level distinguishing mechanism based on the number of the electric vehicles is established at the traffic intersection, and the danger levels are dynamically updated and displayed, the surrounding vehicles and pedestrians are effectively reminded, and the traffic accidents are avoided to a certain extent.
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Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic external view of a signal lamp applied to a traffic intersection dynamic big data updating device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Electric vehicles have a history earlier than our most common internal combustion engine driven vehicles today. Inventor and engineer of parent Hungary of DC motor, Engineer of Anushjederkey Jedlik
The electromagnetically rotating mobile device was tested in the laboratory as early as 1828. The first dc motor-driven electric vehicle was manufactured by Thomas Davenport, tomass, americans in 1834. In 1837, thomas thus patented the first patent in the us motor industry. Between 1832 and 1838, Robert Anderson, Scotch, invented an electrically driven horse car, a vehicle driven by a primary battery that cannot be charged. An electrically driven train was invented by scotch Robert Davidson in 1838. Trams still driving on the road today are patents appearing in the uk in 1840.
History of battery electric vehicles. The first electric automobile in the world was born in 1881, the inventor was a french engineer Gustave trouestaff, teruff, which is a tricycle powered by a lead-acid battery; in 1873, the electric vehicle powered by robert davison primary battery in the english world did not fall under international recognition. Lead-acid batteries, nickel-cadmium batteries, nickel-hydrogen batteries, lithium ion batteries, fuel cells have emerged as electrical power.
In the prior art, when a traffic intersection queues up in red or passes through in green, the driving track of an electric vehicle is the most complex and unsafe, and therefore the driving track should be the key monitoring object, and the number of electric vehicles in the set area of the traffic intersection reflects the current danger level of the traffic intersection.
In order to overcome the defects, the invention builds a traffic intersection dynamic big data updating method, which comprises the steps of using a traffic intersection dynamic big data updating device to establish a danger grade distinguishing mechanism based on the number of electric vehicles at a traffic intersection, and dynamically updating and displaying the danger grade, thereby realizing effective reminding of surrounding vehicles and pedestrians, and avoiding traffic accidents. The dynamic big data updating device for the traffic intersection can effectively solve the corresponding technical problems.
Fig. 1 is a schematic external view of a signal lamp applied to a traffic intersection dynamic big data updating device according to an embodiment of the present invention.
The traffic intersection dynamic big data updating device shown according to the embodiment of the invention comprises:
and the LED display screen is arranged on the cross bar, above the traffic intersection, provided with the signal lamp and used for receiving and displaying the danger level of the traffic intersection in real time.
Next, a detailed configuration of the traffic intersection dynamic big data updating device of the present invention will be further described.
The traffic intersection dynamic big data updating device further comprises:
and the full-color capturing equipment is arranged on a cross bar of which a signal lamp is arranged above the traffic intersection and is used for capturing directional data of the direction of the traffic intersection so as to obtain a corresponding directional captured image.
The traffic intersection dynamic big data updating device further comprises:
the vehicle type distinguishing device is connected with the histogram equalization device and is used for respectively matching each standard contour of each electric vehicle with the histogram equalization image so as to take the area with the matching percentage exceeding the limit as an electric vehicle area and count the number of the electric vehicle areas in the histogram equalization device;
the signal conversion equipment is respectively connected with the LED display screen and the carriage identification equipment and is used for determining the corresponding traffic intersection danger level based on the number of the electric vehicle areas in the histogram equalization equipment;
in the signal conversion device, determining a corresponding traffic intersection hazard level based on the number of electric vehicle zones in the histogram equalization device comprises: the more the number of the electric vehicle areas in the histogram equalization equipment is, the higher the determined traffic intersection danger level is;
the parameter extraction equipment is arranged on a cross bar of a signal lamp arranged above a traffic intersection, is connected with the full-color capture equipment, and is used for receiving the directional capture image and analyzing the number of bytes occupied by the pixel value of each pixel point in the directional capture image to obtain the reference number of bytes to be output;
the pixel point counting equipment is used for receiving the directional captured image and counting the total number of pixel points in the directional captured image to obtain a reference total number output;
the grade identification device is respectively connected with the parameter extraction device and the pixel point counting device and is used for receiving the reference byte number and the reference total number and determining the grade of the data volume in direct proportion to the reference byte number and the reference total number based on the product of the reference byte number and the reference total number;
the gamma correction device is connected with the grade identification device and used for starting receiving the directional captured images when the grade of the received data volume is higher than a preset grade threshold value, and performing gamma correction processing on the directional captured images received from the parameter extraction device to obtain gamma corrected images;
the data identification device is connected with the gamma correction device and used for receiving the gamma correction image, adjusting the edge enhancement processing strength of the V component sub-image in the gamma correction image YUV space based on the data quantity grade, adjusting the edge enhancement processing strength of the U component sub-image in the gamma correction image YUV space based on the data quantity grade, and adjusting the edge enhancement processing strength of the Y component sub-image in the gamma correction image YUV space based on the data quantity grade;
the self-adaptive processing equipment is connected with the data identification equipment and is used for executing edge enhancement processing with respective edge enhancement processing strength on the V component sub-image, the U component sub-image and the Y component sub-image in the gamma correction image YUV space in parallel so as to obtain a corresponding self-adaptive processing image;
a histogram equalization device for receiving the adaptive processing image, performing histogram equalization processing on the adaptive processing image to obtain and output a histogram equalization image;
in the data identification device, the degree of change of the edge enhancement processing strength of the V-component sub-image along with the data volume grade is the same as the degree of change of the edge enhancement processing strength of the U-component sub-image along with the data volume grade;
wherein, in the data identification device, the degree of change of the edge enhancement processing strength of the Y component sub-image along with the data volume level is lower than that of the V component sub-image along with the data volume level;
in the data identification device, the degree of change of the edge enhancement processing strength of the V-component sub-image along with the data volume grade, the degree of change of the edge enhancement processing strength of the U-component sub-image along with the data volume grade and the degree of change of the edge enhancement processing strength of the Y-component sub-image along with the data volume grade are in a direct proportion relation.
In the traffic intersection dynamic big data updating device:
and the grade identification device, the parameter extraction device and the pixel point counting device are all connected with the same 32-bit parallel data bus.
The traffic intersection dynamic big data updating device further comprises:
and the priority detection device is respectively connected with the signal conversion device, the carriage identification device and the histogram equalization device and is used for respectively judging the priorities of the signal conversion device, the carriage identification device and the histogram equalization device according to the importance degree of data respectively processed by the signal conversion device, the carriage identification device and the histogram equalization device.
The traffic intersection dynamic big data updating device further comprises:
and the distribution triggering device is used for determining the distribution ratio of the residual electric quantity to each device of the signal conversion device, the carriage identification device and the histogram equalization device based on the respective priorities of the signal conversion device, the carriage identification device and the histogram equalization device when the electric quantity shortage signal is received.
The traffic intersection dynamic big data updating device further comprises:
the state recognition equipment is connected with the battery equipment and used for analyzing the electric quantity of the battery equipment and sending an electric quantity sufficient signal when the electric quantity of the battery equipment exceeds a limit quantity;
and the clock generating device is respectively connected with the priority detection device, the distribution triggering device and the state identification device and is used for simultaneously providing consistent system clock signals for the priority detection device, the distribution triggering device and the state identification device.
In the traffic intersection dynamic big data updating device:
the state identification equipment is also used for sending out an electric quantity shortage signal when the electric quantity of the battery equipment does not exceed the limit quantity;
in the distribution triggering device, the higher the priority of the signal conversion device, the compartment identification device or the histogram equalization device is, the larger the corresponding distribution ratio is, and the distributed electric quantity is a numerical value obtained by multiplying the residual electric quantity by the distribution ratio;
and the distribution triggering device is also used for stopping the electric quantity analysis action of the battery device when receiving the electric quantity sufficient signal.
Meanwhile, in order to overcome the defects, the invention also provides a computer readable storage medium, wherein a computer program is stored on the readable storage medium, and when the computer program is executed, the computer program realizes the steps of the traffic intersection dynamic big data updating method.
In addition, the histogram equalization apparatus may be implemented using a GAL device. General Array Logic GAL (general Array Logic www.husoon.com) devices were the first electrically erasable, programmable, settable encryption bit PLDs invented by LATTICE. Representative GAL chips are GAL16V8, GAL20, which are capable of emulating almost all types of PAL devices. In practical application, GAL device has 100% compatibility to PAL device emulation, so GAL can almost completely replace PAL device, and can replace most SSI, MSI digital integrated circuit, thus obtaining wide application. The biggest difference between GAL and PAL is that the output structure of the GAL is user-definable and is a programmable output structure. Two basic models of GAL, GAL16V8(20 pins) GAL20V8(24 pins), replace ten PAL devices, and are therefore called pain programmable circuits. The output of the PAL is well defined by the manufacturer, the chip is fixed after being selected, and the user can not change the chip.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: Read-Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A traffic intersection dynamic big data updating method comprises the steps of using a traffic intersection dynamic big data updating device to establish a danger level distinguishing mechanism based on the number of electric vehicles at a traffic intersection, dynamically updating and displaying danger levels, and effectively reminding surrounding vehicles and pedestrians, so that traffic accidents are avoided, wherein the traffic intersection dynamic big data updating device comprises: and the LED display screen is arranged on the cross bar, above the traffic intersection, provided with the signal lamp and used for receiving and displaying the danger level of the traffic intersection in real time.
2. The method of claim 1, wherein the apparatus further comprises: and the full-color capturing equipment is arranged on a cross bar of which a signal lamp is arranged above the traffic intersection and is used for capturing directional data of the direction of the traffic intersection so as to obtain a corresponding directional captured image.
3. The method of claim 2, wherein the apparatus further comprises:
the vehicle type distinguishing device is connected with the histogram equalization device and is used for respectively matching each standard contour of each electric vehicle with the histogram equalization image so as to take the area with the matching percentage exceeding the limit as an electric vehicle area and count the number of the electric vehicle areas in the histogram equalization device;
the signal conversion equipment is respectively connected with the LED display screen and the carriage identification equipment and is used for determining the corresponding traffic intersection danger level based on the number of the electric vehicle areas in the histogram equalization equipment;
in the signal conversion device, determining a corresponding traffic intersection hazard level based on the number of electric vehicle zones in the histogram equalization device comprises: the more the number of the electric vehicle areas in the histogram equalization equipment is, the higher the determined traffic intersection danger level is;
the parameter extraction equipment is arranged on a cross bar of a signal lamp arranged above a traffic intersection, is connected with the full-color capture equipment, and is used for receiving the directional capture image and analyzing the number of bytes occupied by the pixel value of each pixel point in the directional capture image to obtain the reference number of bytes to be output;
the pixel point counting equipment is used for receiving the directional captured image and counting the total number of pixel points in the directional captured image to obtain a reference total number output;
the grade identification device is respectively connected with the parameter extraction device and the pixel point counting device and is used for receiving the reference byte number and the reference total number and determining the grade of the data volume in direct proportion to the reference byte number and the reference total number based on the product of the reference byte number and the reference total number;
the gamma correction device is connected with the grade identification device and used for starting receiving the directional captured images when the grade of the received data volume is higher than a preset grade threshold value, and performing gamma correction processing on the directional captured images received from the parameter extraction device to obtain gamma corrected images;
the data identification device is connected with the gamma correction device and used for receiving the gamma correction image, adjusting the edge enhancement processing strength of the V component sub-image in the gamma correction image YUV space based on the data quantity grade, adjusting the edge enhancement processing strength of the U component sub-image in the gamma correction image YUV space based on the data quantity grade, and adjusting the edge enhancement processing strength of the Y component sub-image in the gamma correction image YUV space based on the data quantity grade;
the self-adaptive processing equipment is connected with the data identification equipment and is used for executing edge enhancement processing with respective edge enhancement processing strength on the V component sub-image, the U component sub-image and the Y component sub-image in the gamma correction image YUV space in parallel so as to obtain a corresponding self-adaptive processing image;
a histogram equalization device for receiving the adaptive processing image, performing histogram equalization processing on the adaptive processing image to obtain and output a histogram equalization image;
in the data identification device, the degree of change of the edge enhancement processing strength of the V-component sub-image along with the data volume grade is the same as the degree of change of the edge enhancement processing strength of the U-component sub-image along with the data volume grade;
wherein, in the data identification device, the degree of change of the edge enhancement processing strength of the Y component sub-image along with the data volume level is lower than that of the V component sub-image along with the data volume level;
in the data identification device, the degree of change of the edge enhancement processing strength of the V-component sub-image along with the data volume grade, the degree of change of the edge enhancement processing strength of the U-component sub-image along with the data volume grade and the degree of change of the edge enhancement processing strength of the Y-component sub-image along with the data volume grade are in a direct proportion relation.
4. The method of claim 3, wherein:
and the grade identification device, the parameter extraction device and the pixel point counting device are all connected with the same 32-bit parallel data bus.
5. The method of claim 4, wherein the apparatus further comprises:
and the priority detection device is respectively connected with the signal conversion device, the carriage identification device and the histogram equalization device and is used for respectively judging the priorities of the signal conversion device, the carriage identification device and the histogram equalization device according to the importance degree of data respectively processed by the signal conversion device, the carriage identification device and the histogram equalization device.
6. The method of claim 5, wherein the apparatus further comprises:
and the distribution triggering device is used for determining the distribution ratio of the residual electric quantity to each device of the signal conversion device, the carriage identification device and the histogram equalization device based on the respective priorities of the signal conversion device, the carriage identification device and the histogram equalization device when the electric quantity shortage signal is received.
7. The method of claim 6, wherein the apparatus further comprises:
the state recognition equipment is connected with the battery equipment and used for analyzing the electric quantity of the battery equipment and sending an electric quantity sufficient signal when the electric quantity of the battery equipment exceeds a limit quantity;
and the clock generating device is respectively connected with the priority detection device, the distribution triggering device and the state identification device and is used for simultaneously providing consistent system clock signals for the priority detection device, the distribution triggering device and the state identification device.
8. The method of claim 7, wherein:
the state identification equipment is also used for sending out an electric quantity shortage signal when the electric quantity of the battery equipment does not exceed the limit quantity;
in the distribution triggering device, the higher the priority of the signal conversion device, the compartment identification device or the histogram equalization device is, the larger the corresponding distribution ratio is, and the distributed electric quantity is a numerical value obtained by multiplying the residual electric quantity by the distribution ratio;
and the distribution triggering device is also used for stopping the electric quantity analysis action of the battery device when receiving the electric quantity sufficient signal.
9. A computer-readable storage medium, having stored thereon a computer program which, when executed, performs the steps of the method of claim 8.
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| 汪莹: "基于安全服务水平的城市道路交叉口交通安全评价方法研究", 《中国博士学位论文全文数据库（电子期刊）工程科技Ⅱ辑》 * |
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| CN111599214B (en) | 2020-12-15 |
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