CN114241760A - Intelligent service area big data fusion system and method - Google Patents

Intelligent service area big data fusion system and method Download PDF

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Publication number
CN114241760A
CN114241760A CN202111521734.0A CN202111521734A CN114241760A CN 114241760 A CN114241760 A CN 114241760A CN 202111521734 A CN202111521734 A CN 202111521734A CN 114241760 A CN114241760 A CN 114241760A
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service area
vehicle
density
vehicles
information
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曹援
陈家宏
茹耀辉
王瀚明
杨庭龙
马东
丁雪鹏
陈雪颖
解亮亮
季星
吴俊澄
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Jiangsu Expressway Information Engineering Co ltd
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Jiangsu Expressway Information Engineering Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • 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
    • G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • 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

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to the technical field of data analysis, and particularly discloses an intelligent service area big data fusion system and a method, wherein the intelligent service area big data fusion system comprises: the mobile terminal is arranged on the automobile. And the driving camera group is arranged at the entrance lane of the service area, acquires the license plate information of the automobile and counts the number of the entering vehicles to be N. And the outgoing camera group is arranged at an outgoing lane entrance of the service area, acquires the license plate information of the automobile and counts the outgoing vehicles into M. And the counting module is arranged in the service area and used for counting the number of people entering the service area. And the control center is used for positioning the vehicles entering the service area, acquiring the driving information of the vehicles and acquiring the information of the entering vehicles and the exiting vehicles. Counting by a counting module to obtain the number X of passenger flows; and obtaining the reception density Y through the number of vehicles and the number of passenger flows. According to the invention, the service area is evaluated through the reception density Y, and the passenger can check the service area reception density Y and select the service area for parking.

Description

Intelligent service area big data fusion system and method
Technical Field
The invention relates to the technical field of data analysis, in particular to a big data fusion system and method for an intelligent service area.
Background
The expressway service area is an essential important accessory facility for guaranteeing driving safety, improving expressway service level, guaranteeing rapid transportation and economy, relieving physiological over-fatigue of drivers and the extreme state of automobiles in use, providing refueling, detecting and maintaining services for vehicles and guaranteeing safety and smoothness of the expressway. The core content of the service area layout planning is to optimize a plurality of possible layout schemes on a given highway network, and finally determine the optimal layout with economy, reasonableness and comprehensive service functions.
The road network properties and traffic properties of the service area are not well known: at the initial stage of the construction of the highway service facilities, because the highway is not formed with the net, the driving distance of the vehicles on the highway is generally short, and meanwhile, the traffic volume on the highway is not large, the service facilities are constructed, and the idle rate is high. The popularity of the understanding that service facilities are repaired and enlarged is widely considered, and the service facilities are more and more spaced and the construction scale is smaller in the following years. However, as the mileage of the highway is gradually increased, the network formation and the traffic volume are gradually increased, the utilization rate of the service facilities is higher and higher, and some services are even embarrassed. Especially, in the peak of passenger flow, the service area can not provide timely service for drivers and passengers, and the service level and quality of the expressway are seriously influenced. The service evaluation is not only the comprehensive evaluation of parking spaces, the number of passengers to be waited, and the like, but also the existing evaluation method has serious defects.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides an intelligent service area big data fusion system, which includes: the system comprises a mobile terminal, an incoming camera group, an outgoing camera group, a counting module and a control center;
the mobile terminal is arranged on the automobile and used for recording automobile running information;
the driving camera group is arranged at a service area entrance lane crossing and is used for shooting an entering vehicle, acquiring the information of a license plate of the vehicle and counting the entering vehicle to be N;
the exit camera group is arranged at an exit lane entrance of the service area and is used for shooting the exiting vehicles, acquiring the license plate information of the vehicles and counting the exiting vehicles into M;
the counting module is arranged in the service area and used for counting the number of people entering the service area;
the control center is used for positioning the vehicles entering the service area, acquiring the driving information of the vehicles and acquiring the information of the vehicles entering and exiting; the control center obtains automobile license plate information from the incoming camera group and the outgoing camera group, binds the automobile license plate information with corresponding automobile running information, obtains the number of vehicles in a service area through N-M, and obtains the number X of passenger flows through counting by a counting module; obtaining the reception density Y through the number of vehicles and the number of passenger flows
Figure BDA0003407561250000021
Wherein m is a vehicle accommodation factor, and n is a human number accommodation factor; and the control center sends the reception density, the number of vehicles and the number of passenger flows to the mobile terminal.
Preferably: the control center obtains a stopping time point T on the vehicle through the vehicle running information1Acquiring the time point T of entering the vehicle through the driving camera group2And obtaining the mark T of the automobile driving-out time point through the driving-out camera group3Then, the running time TLine of＝T2-T1Rest time TRest on the table＝T3-T2By passing
Figure BDA0003407561250000022
Comparing with a preset fatigue driving degree if
Figure BDA0003407561250000023
And if the fatigue driving degree is higher than the fatigue driving degree, the control center sends out a reminding signal.
Preferably: obtaining a reception curve by fitting passenger flow density, vehicle density and/or reception density and time, wherein the reception curve is that the horizontal axis is time, the vertical axis is passenger flow density, vehicle density and/or reception density, and the passenger flow density is
Figure BDA0003407561250000024
Density of the vehicle is
Figure BDA0003407561250000025
Preferably: and predicting through the reception curve, correcting the predicted reception curve in real time through the actual reception curve, and outputting by the control center by taking the initial time point of the time period as an extension signal when the time period when the predicted reception curve exceeds the accommodation saturation exceeds a preset value.
Preferably: when the time period that the prediction curve exceeds the accommodation saturation exceeds 50%, the service area is judged to be continuously saturated.
Preferably: and when the passenger flow density, the vehicle density and/or the reception density are/is compared with a preset accommodation gradient, corresponding prompt is carried out.
Preferably: when the passenger flow density is greater than 80% of the accommodation saturation, the control center sends out a prompt signal; when the passenger flow density is greater than the accommodation saturation, the control center sends a primary alarm signal; when the passenger flow density is more than 120% of the accommodation saturation, the control center sends out a secondary alarm signal.
Preferably: the driving information comprises a starting point, an end point and a planned travel path of the automobile driving, and the corresponding automobile speed in the travel path is recorded.
Preferably: the last stopping time is T which is the time point when the running speed of the automobile is zero and exceeds a specified time1。
The invention also provides an intelligent service area big data fusion method, which applies the intelligent service area big data fusion system and comprises the following steps:
s1, positioning the vehicle entering the service area, and acquiring the vehicle running information, the vehicle entering information and the vehicle exiting information;
s2, obtaining the license plate information of the automobile and binding the license plate information with the corresponding automobile driving information;
s3, obtaining the number of vehicles in the service area through the entering vehicle N and the exiting vehicle M, and obtaining the number X of passenger flows through counting by a counting module;
s4, obtaining reception density Y through the number of vehicles and the number of passenger flows
Figure BDA0003407561250000031
Wherein m is a vehicle accommodation factor, and n is a human number accommodation factor;
and S5, evaluating the service area through the reception density Y and sending the service area to the mobile terminal.
The invention has the technical effects and advantages that: the service area is evaluated through the reception density Y, and the passenger can check the service area where the reception density Y of the service area is selected to stop. The control center can send the reception density, the number of vehicles and the number of passenger flows to the mobile terminal, and passengers can miss the passenger flow volume according to the reception density, the number of vehicles and the number of passenger flows.
Drawings
Fig. 1 is a block diagram of a smart service area big data fusion system according to the present invention.
Fig. 2 is a flow chart of a method for fusing big data of an intelligent service area according to the present invention.
Fig. 3 is a block diagram illustrating the fatigue effect in the smart service area big data fusion system according to the present invention.
Fig. 4 is a functional block diagram of reception curve prediction in a smart service area big data fusion system according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
Referring to fig. 1, in the present embodiment, an intelligent service area big data fusion system is provided, the intelligent service area big data fusion system includes: the mobile terminal, the entrance camera group, the exit camera group, the counting module and the control center.
The mobile terminal is arranged on the automobile and used for recording automobile running information, wherein the running information comprises a starting point, an end point and a planned journey path of automobile running, and the corresponding automobile speed in the journey path is recorded. The mobile terminal may be a car-mounted navigation system, a GPS, or a mobile phone for navigation. When the automobile starts to run, the starting point and the end point of the journey are input into the mobile terminal, the mobile terminal plans the route of the journey by self, and the driver can select the route of the journey suitable for the driver.
And the driving camera group is arranged at the entrance of the service area and is used for shooting the entering vehicles, acquiring the information of the license plates of the vehicles and counting the entering vehicles to be N. The driving camera group can be arranged on a frame of a lane entering a service area, shoots entering vehicles and scans license plates of the vehicles.
And the outgoing camera group is arranged at the service area outgoing vehicle road junction and is used for shooting outgoing vehicles, acquiring the information of vehicle license plates and counting the outgoing vehicles into M. The exit camera group can be arranged on a frame of an exit lane of the service area, and is used for shooting the exit vehicle and scanning the license plate of the vehicle.
The counting module is arranged in the service area and used for counting the number of people entering the service area, the counting module can be arranged at an inlet of a service station of the service area, the service station comprises a toilet, a supermarket, a restaurant and the like, and people entering the service area basically pass through the counting module, so that the number of people entering the service area can be counted through the counting module.
And the control center is electrically connected with the incoming camera group, the outgoing camera group and the counting module, positions the vehicles entering the service area, acquires automobile running information and acquires information of the incoming vehicles and the outgoing vehicles. The control center obtains automobile license plate information from the incoming camera group and the outgoing camera group, binds the automobile license plate information with corresponding automobile running information, obtains the number of vehicles in the service area through N-M, and obtains the number X of passenger flows through counting by the counting module. Obtaining the reception density Y through the number of vehicles and the number of passenger flows
Figure BDA0003407561250000051
Where m is a vehicle accommodation factor and n is a human figure accommodation factor. M can be set according to the parking space of the service area, and n can be determined by the area of a supermarket, the area of a toilet, the number of people accommodated in a rest room and the like. The service area is evaluated through the reception density Y, and the passenger can check the service area where the reception density Y of the service area is selected to stop. The control center can send the reception density, the number of vehicles and the number of passenger flows to the mobile terminal, and passengers can miss the passenger flow volume according to the reception density, the number of vehicles and the number of passenger flows.
Referring to fig. 3, the control center obtains a stopping time point T on the vehicle through the vehicle running information1The previous stop time may be the starting time of the vehicle trip, or the time point when the vehicle speed is zero and exceeds a predetermined time may be T1. For example, the vehicle has a time point at which the vehicle running speed is zero, the time is 30 minutes, the 30 minutes exceeds the specified 20 minutes, and the vehicle re-running time is taken as T1. The time point T of the vehicle entering is acquired through the driving camera group2And obtaining the mark T of the automobile driving-out time point through the driving-out camera group3Time of travel TLine of＝T2-T1Rest time TRest on the table＝T3-T2By passing
Figure BDA0003407561250000052
Comparing with a preset fatigue driving degree if
Figure BDA0003407561250000053
And if the fatigue driving degree is higher than the fatigue driving degree, the control center sends out a reminding signal.
Referring to fig. 4, a reception curve is obtained by fitting the passenger flow density, the vehicle density and/or the reception density with time, and the reception curve may be time on the horizontal axis and passenger flow density, vehicle density and/or reception density on the vertical axis, and the passenger flow density is
Figure BDA0003407561250000054
Density of the vehicle is
Figure BDA0003407561250000055
Therefore, evaluation is carried out, a prediction curve is drawn by obtaining the trend of the passenger flow density, the vehicle density and/or the reception density, and the service items and the accommodation capacity of the service area can be adjusted through the prediction curve. For example, a greater daily midday passenger flow density, vehicle density, and/or reception density may increase service personnel, increase the shelves for goods, increase partial services outdoors, and the like. Some predict the curve changes with the date, generallyAfter the reception curve of the previous year, the service of the current year can be correspondingly adjusted, thereby improving the service quality. And predicting through the passenger flow density, the vehicle density and/or the reception curve, correcting the predicted curve in real time through the actual passenger flow density, the vehicle density and/or the reception curve, and outputting by the control center by taking the initial time point of the time period as an extension signal when the time period when the predicted reception curve exceeds the accommodation saturation exceeds a preset value. The control center carries out prediction extension signals, and the service area can be extended, so that the increase of passenger flow and vehicle flow is avoided, and the capacity of the service area cannot meet the requirement of the passenger flow. For example, after two years of extension signals predicted by the prediction curve, when the time period of the prediction curve exceeding the accommodation saturation exceeds 50%, the service area can be judged to be continuously saturated, the service area extension project can be planned in advance, and the defect that the service area cannot meet the passenger flow is overcome. And when the passenger flow density, the vehicle density and/or the reception density are/is compared with a preset accommodation gradient, corresponding prompt is carried out. For example, when the passenger flow density is greater than 80% of the accommodation saturation, the control center sends out prompt signals, so that the number of supermarket shelves and rest room seats can be increased; when the passenger flow density is larger than the accommodation saturation, the control center sends a first-level alarm signal, and the area of the supermarket, the area of the rest room and the like need to be increased. When the passenger flow density is 120% higher than the accommodation saturation, the control center sends out a secondary alarm signal, and the building area needs to be increased urgently. When the control center sends out an alarm signal, corresponding service facilities can be added. The control center can display through the display, and the display can be set on the service area exit lane, and can also be sent to the mobile terminal through the wireless network, and the display is performed through the mobile terminal. Of course, the broadcast can also be performed by voice.
Example 3
Referring to fig. 2, in the present embodiment, a method for fusing big data of an intelligent service area is provided, including the following steps:
and S1, positioning the vehicle entering the service area, and acquiring the vehicle running information, the vehicle entering information and the vehicle exiting information.
And S2, obtaining the automobile license plate information and binding the automobile license plate information with the corresponding automobile driving information.
And S3, acquiring the number of vehicles in the service area by the entering vehicle N and the exiting vehicle M, and acquiring the number X of passenger flows by counting through the counting module.
S4, obtaining reception density Y through the number of vehicles and the number of passenger flows
Figure BDA0003407561250000071
Where m is a vehicle accommodation factor and n is a human figure accommodation factor.
And S5, evaluating the service area through the reception density Y and sending the service area to the mobile terminal.
It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention. Structures, devices, and methods of operation not specifically described or illustrated herein are generally practiced in the art without specific recitation or limitation.

Claims (10)

1. The utility model provides an intelligence service area big data fusion system which characterized in that, intelligence service area big data fusion system include: the system comprises a mobile terminal, an incoming camera group, an outgoing camera group, a counting module and a control center;
the mobile terminal is arranged on the automobile and used for recording automobile running information;
the driving camera group is arranged at a service area entrance lane crossing and is used for shooting an entering vehicle, acquiring the information of a license plate of the vehicle and counting the entering vehicle to be N;
the exit camera group is arranged at an exit lane entrance of the service area and is used for shooting the exiting vehicles, acquiring the license plate information of the vehicles and counting the exiting vehicles into M;
the counting module is arranged in the service area and used for counting the number of people entering the service area;
control centerPositioning vehicles entering a service area, acquiring vehicle running information, and acquiring information of entering vehicles and exiting vehicles; the control center obtains automobile license plate information from the incoming camera group and the outgoing camera group, binds the automobile license plate information with corresponding automobile running information, obtains the number of vehicles in a service area through N-M, and obtains the number X of passenger flows through counting by a counting module; obtaining the reception density Y through the number of vehicles and the number of passenger flows
Figure FDA0003407561240000011
Wherein m is a vehicle accommodation factor, and n is a human number accommodation factor; and the control center sends the reception density, the number of vehicles and the number of passenger flows to the mobile terminal.
2. The intelligent service area big data fusion system as claimed in claim 1, wherein the control center obtains a stopping time point T on the vehicle through the vehicle driving information1Acquiring the time point T of entering the vehicle through the driving camera group2And obtaining the mark T of the automobile driving-out time point through the driving-out camera group3Then, the running time TLine of＝T2-T1Rest time TRest on the table＝T3-T2By passing
Figure FDA0003407561240000012
Comparing with a preset fatigue driving degree if
Figure FDA0003407561240000013
And if the fatigue driving degree is higher than the fatigue driving degree, the control center sends out a reminding signal.
3. The smart service area big data fusion system of claim 1, wherein a reception curve is obtained by fitting passenger flow density, vehicle density and/or reception density with time, the reception curve is time on the horizontal axis and passenger flow density, vehicle density and/or reception density on the vertical axis; a density of passenger flow of
Figure FDA0003407561240000014
Density of the vehicle is
Figure FDA0003407561240000021
4. The smart service area big data fusion system as claimed in claim 3, wherein the prediction is performed by a reception curve, the predicted reception curve is modified in real time by an actual reception curve, and when a time period of the predicted reception curve exceeding the accommodation saturation exceeds a preset value, the control center outputs an extension signal at an initial time point of the time period.
5. The system of claim 4, wherein the service area is determined to be continuously saturated when the prediction curve exceeds the saturation tolerance by more than 50%.
6. The intelligent service area big data fusion system as claimed in claim 4, wherein when the passenger flow density, the vehicle density and/or the reception density are compared with a preset accommodation gradient, a prompt is made accordingly.
7. The smart service area big data fusion system as claimed in claim 6, wherein the control center sends out a prompt signal when the passenger flow density is greater than 80% of the accommodation saturation; when the passenger flow density is greater than the accommodation saturation, the control center sends a primary alarm signal; when the passenger flow density is more than 120% of the accommodation saturation, the control center sends out a secondary alarm signal.
8. The intelligent service area big data fusion system of claim 1, wherein the driving information comprises a starting point, an end point and a planned travel path of the vehicle, and the corresponding vehicle speed in the travel path is recorded.
9. The system of claim 8, wherein the previous stopping time is T, which is a time point when the vehicle speed is zero and exceeds a predetermined time1。
10. An intelligent service area big data fusion method, which applies an intelligent service area big data fusion system according to any one of claims 1-9, said intelligent service area big data fusion method comprising the following steps:
s1, positioning the vehicle entering the service area, and acquiring the vehicle running information, the vehicle entering information and the vehicle exiting information;
s2, obtaining the license plate information of the automobile and binding the license plate information with the corresponding automobile driving information;
s3, obtaining the number of vehicles in the service area through the entering vehicle N and the exiting vehicle M, and obtaining the number X of passenger flows through counting by a counting module;
s4, obtaining reception density Y through the number of vehicles and the number of passenger flows
Figure FDA0003407561240000031
Wherein m is a vehicle accommodation factor, and n is a human number accommodation factor;
and S5, evaluating the service area through the reception density Y and sending the service area to the mobile terminal.
CN202111521734.0A 2021-12-13 2021-12-13 Intelligent service area big data fusion system and method Pending CN114241760A (en)

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CN117236903B (en) * 2023-11-09 2024-05-10 浙江浙商互联信息科技有限公司 Intelligent management method and system for high-speed service area

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