CN113593231A - Intelligent traffic management method and system based on Internet of things - Google Patents

Intelligent traffic management method and system based on Internet of things Download PDF

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CN113593231A
CN113593231A CN202110865874.3A CN202110865874A CN113593231A CN 113593231 A CN113593231 A CN 113593231A CN 202110865874 A CN202110865874 A CN 202110865874A CN 113593231 A CN113593231 A CN 113593231A
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不公告发明人
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Wuhan Honghuoyi Intelligent Technology 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
    • G08G1/0133—Traffic data processing for classifying traffic situation
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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    • G—PHYSICS
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    • G08G1/09—Arrangements for giving variable traffic instructions
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    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096855—Systems involving transmission of navigation instructions to the vehicle where the output is provided in a suitable form to the driver
    • G08G1/096872—Systems involving transmission of navigation instructions to the vehicle where the output is provided in a suitable form to the driver where instructions are given per voice
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses an intelligent traffic management method and system based on the Internet of things, which are applied to an intelligent traffic management system based on the Internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server; according to the intelligent traffic management method based on the Internet of things, the vehicle passing pictures are collected in real time through the cameras distributed at all positions of the traffic road network, the congested road sections are obtained in real time through the cloud server according to the vehicle passing pictures, and finally the reminding information is sent to the interactive terminal of the vehicle about to drive to the congested road sections, so that the driver is actively informed of the vehicle congestion conditions on the road in time, the driver can conveniently adjust the driving route in time, and congestion of the traffic network is relieved.

Description

Intelligent traffic management method and system based on Internet of things
Technical Field
The invention relates to the technical field of traffic management of the Internet of things, in particular to an intelligent traffic management method and system based on the Internet of things.
Background
At present, the application of the intelligent traffic internet of things still stays in the process of monitoring and recording the traffic jam condition on a road in real time and feeding the traffic jam condition back to each traffic manager in real time so that the traffic managers can issue traffic information in time.
Therefore, the existing traffic internet of things application scheme cannot actively and timely inform the driver of the traffic jam condition on the road, so that the driver cannot timely adjust the driving route, and the whole traffic network becomes congested.
Disclosure of Invention
The invention mainly aims to provide an intelligent traffic management method and system based on the Internet of things, and aims to solve the problem that the existing traffic Internet of things application scheme cannot actively and timely inform a driver of the traffic jam condition on a road.
The technical scheme provided by the invention is as follows:
an intelligent traffic management method based on the Internet of things is applied to an intelligent traffic management system based on the Internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server; the method comprises the following steps:
the camera shoots vehicle passing pictures of all road sections in real time and sends the vehicle passing pictures to the cloud server;
the cloud server carries out image analysis on the vehicle passing pictures frame by frame so as to judge whether each road section is congested or not;
if so, marking the road section with the congestion condition as a congestion road section;
and sending reminding information to the interactive terminal of the vehicle about to drive to the congested road section.
Preferably, the sending of the reminding information to the interactive terminal of the vehicle about to drive to the congested road segment includes:
the interactive terminal acquires the geographic position of the current vehicle in real time and sends the geographic position to the cloud server in real time;
the cloud server marks vehicles located within a first preset kilometer of the square circle of the congested road section as target vehicles based on the geographic positions sent by the interactive terminals;
and sending the reminding information to the interactive terminal of the target vehicle.
Preferably, the interactive terminal has a sound collection function; the cloud server marks vehicles located within a first preset kilometer of the square circle of the congested road section as target vehicles based on the geographic positions sent by the interactive terminals, and then the cloud server further comprises:
marking the interactive terminal arranged in the target vehicle as a target terminal;
the target terminal acquires the driving destination of the personnel in the target vehicle in a voice acquisition mode and sends the driving destination to the cloud server;
the cloud server establishes a corresponding relation between the geographic position and the driving destination sent by the same interactive terminal;
the cloud server generating at least 3 first travel paths of a total route ranging from small to large to the travel destination based on the travel destination and the corresponding geographic location;
the cloud server judges whether the first driving paths all approach the congested road section;
if yes, the cloud server marks the first driving path with the shortest total route as a first recommended path and sends the first recommended path to the corresponding target terminal;
if not, the cloud server marks the first driving path which does not approach the congested road section and has the shortest total path as a second recommended path, and sends the second recommended path to the corresponding target terminal;
and the target terminal feeds back the first recommended path or the second recommended path to personnel in the vehicle.
Preferably, the interactive terminal has voice broadcasting and image displaying functions; the target terminal feeds back the first recommended route or the second recommended route to people in the vehicle, and the method comprises the following steps:
the interactive terminal displays the first recommended path or the second recommended path in an image display mode;
when the interactive terminal receives the first recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: the current running necessarily passes through the congested road section;
when the interactive terminal receives the second recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: attention is paid to avoiding congested road segments.
Preferably, the cloud server establishes a correspondence between the geographic location and the travel destination sent by the same interactive terminal, and then further includes:
the cloud server judges whether the phenomenon that the number of the driving destinations exceeds a preset number exists in the range of a second preset kilometer of the square circle;
if yes, marking all the driving destinations existing in the range of a second preset kilometer of the square circle as destinations to be analyzed;
the cloud server marks the target vehicle corresponding to the destination to be analyzed as a vehicle to be analyzed;
the cloud server divides the vehicles to be analyzed into a first vehicle group, a second vehicle group and a third vehicle group, wherein the difference of the number of vehicles in any two of the first vehicle group, the second vehicle group and the third vehicle group is not more than 1;
the cloud server generates a second driving path which is shortest in total distance and leads to the destination to be analyzed and does not pass through the congested road section on the basis of the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the first vehicle group, and generating a third travel path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the second vehicle group, and generating a fourth driving path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the third vehicle group, wherein a road section coincidence rate among the second travel path, the third travel path and the fourth travel path does not exceed a first preset value;
the cloud server sends the first running path to the corresponding interactive terminal of the vehicle to be analyzed, sends the second running path to the corresponding interactive terminal of the vehicle to be analyzed, and sends the third running path to the corresponding interactive terminal of the vehicle to be analyzed;
the interactive terminal receiving the first running path feeds back the first running path as a navigation path to personnel in the vehicle;
the interactive terminal receiving the second driving path feeds back the second driving path as a navigation path to personnel in the vehicle;
and the interactive terminal receiving the third driving path feeds back the third driving path as a navigation path to personnel in the vehicle.
Preferably, the interactive terminal has an image acquisition function and a speaker function; the method further comprises the following steps:
the interactive terminal collects images in front of the vehicle in real time and marks the images as front images;
the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver;
the interactive terminal carries out image analysis on the front image so as to judge whether a traffic signal red light appears in the front image;
if so, the interactive terminal marks the moment when the front image starts to appear the traffic signal red light as the starting moment;
the interactive terminal marks the moment when the yellow light of the traffic signal begins to appear after the starting moment as a target moment;
the interactive terminal carries out image analysis on the driver image so as to judge whether the visual angle of a driver in the vehicle is not towards the front of the vehicle all the time in a first preset time period after the target time;
if yes, the interactive terminal sends out a first prompt tone.
Preferably, the interactive terminal has a sound acquisition function and a speed acquisition function; the interactive terminal comprises an angle sensor for monitoring the rotation angle of a steering wheel of the vehicle and a grip strength sensor arranged on the steering wheel of the vehicle; the method further comprises the following steps:
the interactive terminal obtains the current time and generates a time factor Y based on the current timetWherein the time factor Y is set when the current time falls within a preset time intervalt1, when the current time does not fall into the preset time interval, the time factor YtTaking 0;
the interactive terminal carries out image analysis on the driver image in a second preset time period before the current time so as to obtain the head displacement S of the driver in the second preset time period before the current timetAnd generating a standard head displacement S based on the second preset time periodb；
The interactive terminal obtains the accumulated rotation angle J of the steering wheel in the second preset time period before the current moment of the vehicle through the angle sensorlAnd generating a standard rotation angle J based on the second preset time periodb；
The interactive terminal obtains the times C that the grip strength of the driver holding the steering wheel is smaller than the preset grip strength in the second preset time period before the current moment of the vehicle through the grip strength sensorw；
The cross-linkingThe mutual terminal carries out image analysis on the driver image in the second preset time period before the current time so as to obtain the yawning times C of the driver in the second preset time period before the current timeh；
The interactive terminal collects images of the copiers in the vehicle in real time and marks the images as copiers images;
the interactive terminal carries out image analysis on the copilot image in the second preset time period before the current moment so as to judge whether a copilot person exists in the vehicle in the second preset time period before the current moment;
if yes, the interactive terminal generates a secondary driving constant A, the secondary driving constant A is 0, sound collection is carried out on a secondary driving area in the vehicle, and the frequency C of the sounds made by secondary driving personnel in the vehicle in the second preset time period before the current time is judgedyAnd generating a first standard time C based on the second preset time period1；
If not, the interactive terminal generates a secondary driving constant A, and the secondary driving constant A is 1;
the interactive terminal acquires road condition images in front of the vehicle in real time and analyzes the running speed of the vehicle in real time so as to judge whether unnecessary braking of the vehicle occurs in the second preset time period before the current moment;
if so, the interactive terminal generates a brake constant B, and the brake constant B is 10;
if not, the interactive terminal generates a brake constant B, and the brake constant B is 0;
the interactive terminal generates a fatigue driving indicated value Z:
Figure BDA0003187468830000051
the larger the fatigue driving indication value Z is, the larger the probability that the driver is fatigue driving is;
and when the fatigue driving indicated value is larger than a second preset value, the interactive terminal sends out a second prompt tone.
Preferably, the method further comprises the following steps:
the interactive terminal acquires the real-time distance between the vehicle and the nearest front vehicle;
the interactive terminal acquires a safety distance value corresponding to the vehicle;
and when the real-time distance is smaller than the safe distance value, the interactive terminal sends out a third prompt tone.
Preferably, the interactive terminal has an image acquisition function, a sound acquisition function and a speed acquisition function; the interactive terminal obtains the safe distance value corresponding to the vehicle, and the method comprises the following steps:
the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver;
the interactive terminal carries out image analysis on the driver image in a third preset time period before the current time so as to obtain the number C of times that the driver watches the mobile phone in the third preset time period before the current timekAnd generating a second standard time C based on the third preset time period2；
The interactive terminal acquires the times C of traffic accidents occurring in the third preset time period before the current moment of the vehicle through the cloud server and the traffic management big datasAnd generating a third standard time C based on the third preset time period3；
The interactive terminal analyzes the running speed of the vehicle in the third preset time period before the current moment so as to obtain the total times C of sudden braking and sudden acceleration of the vehiclezAnd generating a fourth standard time C based on the third preset time period4；
The interactive terminal obtains the average value t of the time interval from the time when the vehicle lights a brake tail lamp in the nearest preceding vehicle to the time when the vehicle has a sudden speed drop in the third preset time period before the current time through the image acquisition function and the speed acquisition functionjAnd obtaining a standard average value tb；
The interactive terminal generates a safe distance value D corresponding to the cost vehicle:
Figure BDA0003187468830000061
wherein D is1Is a standard distance value in meters, and is constant.
The invention also provides an intelligent traffic management system based on the Internet of things, which is applied to the intelligent traffic management method based on the Internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server.
Through above-mentioned technical scheme, can realize following beneficial effect:
according to the intelligent traffic management method based on the Internet of things, the vehicle passing pictures are collected in real time through the cameras distributed at all positions of the traffic road network, the congested road sections are obtained in real time through the cloud server according to the vehicle passing pictures, and finally the reminding information is sent to the interactive terminal of the vehicle about to drive to the congested road sections, so that the driver is actively informed of the vehicle congestion conditions on the road in time, the driver can conveniently adjust the driving route in time, and congestion of the traffic network is relieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of a first embodiment of an intelligent traffic management method based on the internet of things according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides an intelligent traffic management method and system based on the Internet of things.
As shown in fig. 1, in a first embodiment of the intelligent traffic management method based on the internet of things, the present embodiment is applied to an intelligent traffic management system based on the internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server; the embodiment comprises the following steps:
step S110: the camera shoots vehicle passing pictures of all road sections in real time and sends the vehicle passing pictures to the cloud server.
Specifically, the vehicle passage screen here is a screen for feedback of the road congestion condition.
Step S120: and the cloud server performs image analysis on the vehicle passing pictures frame by frame so as to judge whether each road section is congested or not.
Specifically, the congestion condition refers to an average traveling speed of the vehicle being less than 5 km/h.
If yes, go to step S130: and marking the road sections with congestion conditions as the congestion road sections.
Specifically, the cloud server marks the road section with the congestion condition as the congestion road section.
Step S140: and sending reminding information to the interactive terminal of the vehicle about to drive to the congested road section.
Specifically, the vehicle about to drive to the congested road section refers to a vehicle that may drive to the congested road section, and includes a case where a current driving path of the vehicle includes the congested road section; and the cloud server sends reminding information to the interactive terminal of the vehicle about to drive to the congested road section. The reminding information can be in a voice playing mode to remind people in the vehicle that the road section in front of the people is congested.
According to the intelligent traffic management method based on the Internet of things, the vehicle passing pictures are collected in real time through the cameras distributed at all positions of the traffic road network, the congested road sections are obtained in real time through the cloud server according to the vehicle passing pictures, and finally the reminding information is sent to the interactive terminal of the vehicle about to drive to the congested road sections, so that the driver is actively informed of the vehicle congestion conditions on the road in time, the driver can conveniently adjust the driving route in time, and congestion of the traffic network is relieved.
In a second embodiment of the intelligent traffic management method based on the internet of things provided by the present invention, based on the first embodiment, step S140 includes the following steps:
step S210: the interactive terminal collects the geographic position of the current vehicle in real time and sends the geographic position to the cloud server in real time.
Specifically, the geographic location is the current location of the vehicle.
Step S220: and the cloud server marks the vehicles positioned in the first preset kilometer of the square circle of the congested road section as target vehicles based on the geographic positions sent by the interactive terminals.
Specifically, the first preset kilometer is 5 kilometers. That is, the target vehicle is the vehicle that will be driven to the congested road segment in the first embodiment, the vehicle that will be driven to the congested road segment is a vehicle within 5 km of the congested road segment square circle,
step S230: and sending the reminding information to the interactive terminal of the target vehicle.
Specifically, the cloud server sends the reminding information to the interactive terminal of the target vehicle. The purpose of this embodiment is to give a solution how to determine the vehicles that are going to drive towards a congested road section.
In a third embodiment of the intelligent traffic management method based on the internet of things, based on the second embodiment, the interactive terminal has a sound collection function; step S220, the following steps are also included thereafter:
step S310: and marking the interactive terminal arranged in the target vehicle as a target terminal.
Specifically, the cloud server marks the interactive terminal set in the target vehicle as a target terminal.
Step S320: the target terminal acquires the driving destination of the personnel in the target vehicle in a voice acquisition mode and sends the driving destination to the cloud server.
Specifically, the person in the target vehicle is caused to acquire the driving destination of the person in the target vehicle by dictating the destination.
Step S330: and the cloud server establishes a corresponding relation between the geographic position and the driving destination sent by the same interactive terminal.
Step S340: the cloud server generates at least 3 first travel paths leading to the travel destination, the first travel paths being arranged from small to large in total travel, based on the travel destination and the corresponding geographic location.
Specifically, the cloud server generates, for the driving destination and the corresponding current geographic position of each vehicle, at least 3 (preferably 3 in this embodiment) first driving paths leading to the driving destination, where the total distance is arranged from small to large, and the first driving path is an alternative path for the target vehicle to navigate.
Step S350: the cloud server judges whether the first driving paths all approach the congested road section.
If yes, go to step S360: the cloud server marks the first driving path with the shortest total route as a first recommended path and sends the first recommended path to the corresponding target terminal.
Specifically, if yes, it is stated that all the 3 first traveling paths will pass through the congested road segment, and therefore, the first traveling path with the shortest total path among the 3 first traveling paths is directly used as the first submission path and sent to the corresponding target terminal.
If not, go to step S370: and the cloud server marks the first driving path which does not approach the congested road section and has the shortest total route as a second recommended path, and sends the second recommended path to the corresponding target terminal.
Specifically, if not, it is described that at least one first driving route which does not pass through the congested road segment exists in the 3 first driving routes, so that the first driving route which does not pass through the congested road segment and has the shortest total route is marked as a second recommended route, and the second route is sent to the corresponding target terminal.
Step S380: and the target terminal feeds back the first recommended path or the second recommended path to personnel in the vehicle.
Specifically, the target terminal feeds back the first recommended route or the second recommended route to the people in the vehicle so as to provide driving navigation for the people in the vehicle. The present embodiment aims to provide a suitable navigation travel path to a person in a target vehicle.
In a fourth embodiment of the intelligent traffic management method based on the internet of things, based on the third embodiment, the interactive terminal has voice broadcasting and image display functions; step S380, including the steps of:
step S410: and the interactive terminal displays the first recommended path or the second recommended path in an image display mode.
Specifically, the first recommended route or the second recommended route is displayed in an image display mode so as to provide navigation service for people in the vehicle.
Step S420: when the interactive terminal receives the first recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: the current driving necessarily passes through the congested road section.
Specifically, because the first recommended route necessarily passes through the congested road segment, the people in the vehicle need to be reminded in time: the vehicle runs through the congested road sections inevitably, so that people in the vehicle are prevented from searching other paths in a mess, and the running time is prolonged.
Step S430: when the interactive terminal receives the second recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: attention is paid to avoiding congested road segments.
Specifically, because the second recommended route does not pass through the congested road segment, the people in the vehicle are reminded: care is taken to avoid congested road segments to ensure quick arrival at the destination. The purpose of this embodiment is to provide a specific solution for navigating the person in the target vehicle.
In a fifth embodiment of the intelligent traffic management method based on the internet of things provided by the present invention, based on the third embodiment, step S330 further includes the following steps:
step S510: and the cloud server judges whether the phenomenon that the number of the driving destinations exceeds the preset number exists in the range of a second preset kilometer of the square circle.
Specifically, the second preset kilometer is preferably 1 kilometer, and the preset number is 100; that is, there are more than 100 travel destinations within 1 km of a square circle, which is very likely to cause congestion, and therefore, it is necessary to perform split navigation on vehicles at these travel destinations.
If yes, go to step S520: and marking all the driving destinations existing in the range of the second preset kilometer of the square circle as destinations to be analyzed.
Specifically, the destination to be analyzed here is a travel destination which is within 1 km from the square circle and is more than 100 in number.
Step S530: the cloud server marks the target vehicle corresponding to the destination to be analyzed as a vehicle to be analyzed.
Specifically, the target vehicle corresponding to the destination to be analyzed is the vehicle to be analyzed.
Step S540: the cloud server divides the vehicles to be analyzed into a first vehicle group, a second vehicle group and a third vehicle group, wherein the difference of the vehicle numbers of any two of the first vehicle group, the second vehicle group and the third vehicle group is not more than 1.
Specifically, the purpose of this step is to divide the vehicles to be analyzed into 3 groups, which are a first vehicle group, a second vehicle group and a third vehicle group, respectively, where the difference between the numbers of vehicles in any two of the first vehicle group, the second vehicle group and the third vehicle group is not greater than 1, that is, the difference between the numbers of vehicles in the 3 groups of vehicles to be analyzed is small, so as to send different navigation paths to the 3 groups of vehicles in the subsequent step, respectively, thereby avoiding that all the vehicles to be analyzed reach respective destinations within 1 km of a square circle through the same navigation path, and thus reducing the probability of traffic congestion.
Step S550: the cloud server generates a second driving path which is shortest in total distance and leads to the destination to be analyzed and does not pass through the congested road section on the basis of the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the first vehicle group, and generating a third travel path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the second vehicle group, and generating a fourth driving path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the third vehicle group, wherein a road section coincidence rate between the second travel path, the third travel path, and the fourth travel path does not exceed a first preset value.
Specifically, the first preset value here is 30%; the second travel path, the third travel path and the fourth travel path are navigation paths with path overlapping rate lower than 30%, the second travel path corresponds to the first vehicle group, the third travel path corresponds to the second vehicle group, and the fourth travel path corresponds to the third vehicle group, so that the overlapping rate of the navigation paths corresponding to the vehicles in different groups is lower than 30%, and the probability that 3 groups of vehicles are bundled in the same road section is reduced.
Step S560: the cloud server sends the first running path to the corresponding interactive terminal of the vehicle to be analyzed, sends the second running path to the corresponding interactive terminal of the vehicle to be analyzed, and sends the third running path to the corresponding interactive terminal of the vehicle to be analyzed.
Step S570: and the interactive terminal receiving the first running path feeds back the first running path as a navigation path to personnel in the vehicle.
Step S580: and the interactive terminal receiving the second driving path feeds back the second driving path as a navigation path to personnel in the vehicle.
Step S590: and the interactive terminal receiving the third driving path feeds back the third driving path as a navigation path to personnel in the vehicle.
The purpose of this embodiment is to allocate a navigation route with a coincidence rate lower than 30% to a corresponding vehicle when there are too many travel destinations within a second preset kilometer, so as to avoid traffic congestion.
In a sixth embodiment of the intelligent traffic management method based on the internet of things, based on the first embodiment, the interactive terminal has an image acquisition function and a speaker function; the embodiment further comprises the following steps:
step S610: the interactive terminal collects images in front of the vehicle in real time and marks the images as front images.
Step S620: the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver.
Step S630: and the interactive terminal performs image analysis on the front image to judge whether a traffic signal red light appears in the front image.
If yes, go to step S640: and the interactive terminal marks the moment when the front image starts to appear the traffic signal red light as the starting moment.
Step S640: and the interactive terminal marks the moment when the yellow light of the traffic signal starts to appear after the starting moment as a target moment.
Specifically, the target time is a time when the yellow light starts to appear after the starting time.
Step S650: and the interactive terminal carries out image analysis on the driver image so as to judge whether the visual angle of the driver in the vehicle is not towards the front of the vehicle all the time in a first preset time period after the target time.
The first preset time period here is 3 seconds.
If yes, go to step S660: and the interactive terminal sends out a first prompt tone.
Specifically, if yes, after the vehicle waits for a red light, within 3 seconds after a yellow light appears, the visual angle of a driver in the vehicle is consistent and does not face the front of the vehicle, namely the driver does not notice that a signal lamp is changed into the yellow light, the driver is distracted, in order to not delay the vehicle behind the vehicle passing through a traffic signal lamp, a first prompt sound is directly sent out through the interactive terminal, and the content of the first prompt sound is to remind the driver to pay attention to the traffic signal lamp.
In a seventh embodiment of the intelligent traffic management method based on the internet of things, based on the sixth embodiment, the interactive terminal has a sound acquisition function and a speed acquisition function; the interactive terminal comprises an angle sensor for monitoring the rotation angle of a steering wheel of the vehicle and a grip strength sensor arranged on the steering wheel of the vehicle; the embodiment further comprises the following steps:
step S701: the interactive terminal obtains the current time and generates a time factor Y based on the current timetWherein the time factor Y is set when the current time falls within a preset time intervalt1, when the current time does not fall into the preset time interval, the time factor YtTake 0.
Specifically, the preset time interval corresponds to a time interval at night, for example, from 12 am to 7 am, and a driver is more likely to fatigue during the time interval; so that the time factor Y is set when the current time falls within the preset time intervalt1, when the current time does not fall into the preset time interval, the time factor YtTake 0.
Step S702: the interactive terminal carries out image analysis on the driver image in a second preset time period before the current time so as to obtain the head displacement S of the driver in the second preset time period before the current timetAnd based on said second presetTime period generation standard head displacement Sb。
Specifically, the second preset time period in this embodiment is 1 hour, and normally, the amount of head displacement of the driver is relatively small, where the amount of head displacement S istThe larger the value, the more times the dozing state of the driver appears; at the same time, the standard head displacement S herebI.e. the amount of head displacement of the driver within 1 hour, corresponding to normal driving conditions.
Step S703: the interactive terminal obtains the accumulated rotation angle J of the steering wheel in the second preset time period before the current moment of the vehicle through the angle sensorlAnd generating a standard rotation angle J based on the second preset time periodb。
Specifically, when the driver drives normally, the corresponding accumulated rotation angle JlShould be stable, and when the driver is tired and sleepy, the cumulative angle of rotation J herelWill be smaller; at the same time, the standard rotation angle J herebIs the cumulative angle of the steering wheel within 1 hour of the driver during normal driving.
Step S704: the interactive terminal obtains the times C that the grip strength of the driver holding the steering wheel is smaller than the preset grip strength in the second preset time period before the current moment of the vehicle through the grip strength sensorw。
Specifically, the preset grip is the minimum value of the grip value interval of the driver holding the steering wheel under the normal driving condition; if the grip strength is smaller than the preset grip strength, the situation that the hands of the driver are relaxed is shown, namely the situation that the driver is tired in driving; so under normal driving conditions, the number C herewShould be 0,; when the driver is tired and sleepy, the number CwIt will increase.
Step S705: the interactive terminal carries out image analysis on the driver image in the second preset time period before the current time so as to obtain the yawning times C of the driver in the second preset time period before the current timeh。
In particular, the number of yawns C is here counted under normal driving conditionshShould be 0.
Step S706: the interactive terminal collects images of the copiers in the vehicle in real time and marks the images as copiers.
Step S707: and the interactive terminal carries out image analysis on the copilot image in the second preset time period before the current moment so as to judge whether a copilot person exists in the vehicle in the second preset time period before the current moment.
Specifically, when there is a passenger, the driver is not likely to experience fatigue driving due to communication between the passenger and the driver.
If yes, go to step S708: the interactive terminal generates a secondary driving constant A, the secondary driving constant A is 0, sound collection is carried out on a secondary driving area in the vehicle, and the frequency C of the sounds of secondary driving personnel in the vehicle in the second preset time period before the current moment is judgedyAnd generating a first standard time C based on the second preset time period1。
Specifically, if so, the number of utterances C is set hereyThe larger the voice is, the more the pilot language is, the less fatigue driving is correspondingly caused to the driver; here the first standard degree C1It is the average number of times a passenger uttered a sound within 1 hour under normal driving conditions.
If not, go to step S709: and the interactive terminal generates a copilot constant A, and the copilot constant A is 1.
Step S710: the interactive terminal acquires road condition images in front of the vehicle in real time and analyzes the running speed of the vehicle in real time so as to judge whether unnecessary braking of the vehicle occurs in the second preset time period before the current moment.
Specifically, the unnecessary braking condition refers to a braking condition that a vehicle does not appear on a road ahead, or the vehicle is located outside a safe braking distance; this unnecessary braking situation is a feedback braking situation in which the driver suddenly wakes up when he or she dozes off, and if the unnecessary braking situation occurs, it indicates that the driver is driving fatigued and the degree of fatigue is high.
If yes, go to step S711: and the interactive terminal generates a brake constant B, and the brake constant B is 10.
If not, go to step S712: and the interactive terminal generates a brake constant B, and the brake constant B is 0.
Step S713: the interactive terminal generates a fatigue driving indicated value Z:
Figure BDA0003187468830000151
the greater the fatigue driving instruction value Z is, the greater the probability that the driver is fatigue driving is.
Step S714: and when the fatigue driving indicated value is larger than a second preset value, the interactive terminal sends out a second prompt tone.
Specifically, the second preset value is 10, when the fatigue driving indication value Z is greater than 10, it indicates that the driver is driving fatigue, and for safety, the interactive terminal sends out a second prompt sound, where the content of the second prompt sound is to remind the driver not to drive fatigue.
In an eighth embodiment of the intelligent traffic management method based on the internet of things provided by the present invention, based on the first embodiment, the present embodiment further includes the following steps:
step S810: and the interactive terminal acquires the real-time distance between the vehicle and the nearest front vehicle.
Step S820: and the interactive terminal acquires a safe distance value corresponding to the vehicle.
Specifically, the safe distance value is the distance that the vehicle should decelerate or brake.
Step S830: and when the real-time distance is smaller than the safe distance value, the interactive terminal sends out a third prompt tone.
Specifically, when the real-time distance is smaller than the safe distance value, a third prompt tone is sent through the interactive terminal to remind a driver of carrying out deceleration or braking operation.
In a ninth embodiment of the intelligent traffic management method based on the internet of things, based on the eighth embodiment, the interactive terminal has an image acquisition function, a sound acquisition function and a speed acquisition function; the method for acquiring the safe distance value corresponding to the vehicle by the interactive terminal comprises the following steps:
step S910: the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver.
Step S920: the interactive terminal carries out image analysis on the driver image in a third preset time period before the current time so as to obtain the number C of times that the driver watches the mobile phone in the third preset time period before the current timekAnd generating a second standard time C based on the third preset time period2。
Specifically, the third preset time period is 3 months, and the number of times of watching the mobile phone is CkThe larger the traffic accident, the lower the driving concentration of the driver is, the more easily the traffic accident occurs to the driver; here the second standard degree C2The average number of times a normal driver views a cell phone while driving a vehicle in the past 3 months is a measure for CkThe standard value of (2).
Step S930: the interactive terminal acquires the times C of traffic accidents occurring in the third preset time period before the current moment of the vehicle through the cloud server and the traffic management big datasAnd generating a third standard time C based on the third preset time period3。
Specifically, the number of traffic accidents C issThe larger the traffic accident, the more likely the driver is to have a traffic accident; here the third standard number of times C3The average number of traffic accidents per driver in the past 3 months is a measure CsThe standard value of (2).
Step S940: the interactive terminal analyzes the running speed of the vehicle in the third preset time period before the current time so as to acquire the occurrence of the vehicleTotal number of sudden braking and sudden acceleration CzAnd generating a fourth standard time C based on the third preset time period4。
Specifically, the total number of sudden braking and sudden acceleration C iszThe larger the size, the more impatient the style of the driver driving the vehicle, the more likely the driver is to have a traffic accident; fourth standard degree C here4I.e., the average number of hard braking and hard acceleration per driver over the past 3 months, is a measure for CzThe standard value of (2).
Step S950: the interactive terminal obtains the average value t of the time interval from the time when the vehicle lights a brake tail lamp in the nearest preceding vehicle to the time when the vehicle has a sudden speed drop in the third preset time period before the current time through the image acquisition function and the speed acquisition functionjAnd obtaining a standard average value tb。
Specifically, the average value t herejThat is, the average value of the time interval during which the driver responds to the vehicle to decelerate every time the vehicle brakes before in the past 3 months, and the average value tjThe longer the response time of the driver corresponding to the front vehicle brake is, the more easily the traffic accident occurs to the driver; standard mean value t herebThe average value of the time interval for all drivers to respond to deceleration after the occurrence of the preceding vehicle braking event is a measure of the average value tjThe standard value of (2).
Step S960: the interactive terminal generates a safe distance value D corresponding to the cost vehicle:
Figure BDA0003187468830000161
wherein D is1The unit of the standard distance value is meter, a constant is taken, in this embodiment, the standard distance value is preferably 80 meters, and through the above formula, an individualized safe distance value can be set for each driver based on the driving habits of the driver, that is, the safe distance value of the driver with good driving habits can be relatively shortened; and for driving with bad driving habitThe safe distance value of the driver is required to be longer, so that personalized safe driving reminding is realized.
The invention also provides an intelligent traffic management system based on the Internet of things, which is applied to the intelligent traffic management method based on the Internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, wherein the software product is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. An intelligent traffic management method based on the Internet of things is characterized by being applied to an intelligent traffic management system based on the Internet of things; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server; the method comprises the following steps:
the camera shoots vehicle passing pictures of all road sections in real time and sends the vehicle passing pictures to the cloud server;
the cloud server carries out image analysis on the vehicle passing pictures frame by frame so as to judge whether each road section is congested or not;
if so, marking the road section with the congestion condition as a congestion road section;
and sending reminding information to the interactive terminal of the vehicle about to drive to the congested road section.
2. The intelligent traffic management method based on the internet of things as claimed in claim 1, wherein the sending of the reminding message to the interactive terminal of the vehicle about to drive to the congested road segment comprises:
the interactive terminal acquires the geographic position of the current vehicle in real time and sends the geographic position to the cloud server in real time;
the cloud server marks vehicles located within a first preset kilometer of the square circle of the congested road section as target vehicles based on the geographic positions sent by the interactive terminals;
and sending the reminding information to the interactive terminal of the target vehicle.
3. The intelligent traffic management method based on the internet of things as claimed in claim 2, wherein the interactive terminal has a sound collection function; the cloud server marks vehicles located within a first preset kilometer of the square circle of the congested road section as target vehicles based on the geographic positions sent by the interactive terminals, and then the cloud server further comprises:
marking the interactive terminal arranged in the target vehicle as a target terminal;
the target terminal acquires the driving destination of the personnel in the target vehicle in a voice acquisition mode and sends the driving destination to the cloud server;
the cloud server establishes a corresponding relation between the geographic position and the driving destination sent by the same interactive terminal;
the cloud server generating at least 3 first travel paths of a total route ranging from small to large to the travel destination based on the travel destination and the corresponding geographic location;
the cloud server judges whether the first driving paths all approach the congested road section;
if yes, the cloud server marks the first driving path with the shortest total route as a first recommended path and sends the first recommended path to the corresponding target terminal;
if not, the cloud server marks the first driving path which does not approach the congested road section and has the shortest total path as a second recommended path, and sends the second recommended path to the corresponding target terminal;
and the target terminal feeds back the first recommended path or the second recommended path to personnel in the vehicle.
4. The intelligent traffic management method based on the internet of things of claim 3, wherein the interactive terminal has voice broadcasting and image display functions; the target terminal feeds back the first recommended route or the second recommended route to people in the vehicle, and the method comprises the following steps:
the interactive terminal displays the first recommended path or the second recommended path in an image display mode;
when the interactive terminal receives the first recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: the current running necessarily passes through the congested road section;
when the interactive terminal receives the second recommended path, the interactive terminal reminds people in the vehicle in a voice broadcast mode: attention is paid to avoiding congested road segments.
5. The intelligent traffic management method based on the internet of things as claimed in claim 3, wherein the cloud server establishes a correspondence relationship between the geographic location and the driving destination sent by the same interactive terminal, and then further comprises:
the cloud server judges whether the phenomenon that the number of the driving destinations exceeds a preset number exists in the range of a second preset kilometer of the square circle;
if yes, marking all the driving destinations existing in the range of a second preset kilometer of the square circle as destinations to be analyzed;
the cloud server marks the target vehicle corresponding to the destination to be analyzed as a vehicle to be analyzed;
the cloud server divides the vehicles to be analyzed into a first vehicle group, a second vehicle group and a third vehicle group, wherein the difference of the number of vehicles in any two of the first vehicle group, the second vehicle group and the third vehicle group is not more than 1;
the cloud server generates a second driving path which is shortest in total distance and leads to the destination to be analyzed and does not pass through the congested road section on the basis of the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the first vehicle group, and generating a third travel path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the second vehicle group, and generating a fourth driving path which leads to the destination to be analyzed and does not pass through the congested road section and has the shortest total distance based on the destination to be analyzed and the geographic position corresponding to the vehicle to be analyzed in the third vehicle group, wherein a road section coincidence rate among the second travel path, the third travel path and the fourth travel path does not exceed a first preset value;
the cloud server sends the first running path to the corresponding interactive terminal of the vehicle to be analyzed, sends the second running path to the corresponding interactive terminal of the vehicle to be analyzed, and sends the third running path to the corresponding interactive terminal of the vehicle to be analyzed;
the interactive terminal receiving the first running path feeds back the first running path as a navigation path to personnel in the vehicle;
the interactive terminal receiving the second driving path feeds back the second driving path as a navigation path to personnel in the vehicle;
and the interactive terminal receiving the third driving path feeds back the third driving path as a navigation path to personnel in the vehicle.
6. The intelligent traffic management method based on the internet of things of claim 1, wherein the interactive terminal has an image acquisition function and a speaker function; the method further comprises the following steps:
the interactive terminal collects images in front of the vehicle in real time and marks the images as front images;
the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver;
the interactive terminal carries out image analysis on the front image so as to judge whether a traffic signal red light appears in the front image;
if so, the interactive terminal marks the moment when the front image starts to appear the traffic signal red light as the starting moment;
the interactive terminal marks the moment when the yellow light of the traffic signal begins to appear after the starting moment as a target moment;
the interactive terminal carries out image analysis on the driver image so as to judge whether the visual angle of a driver in the vehicle is not towards the front of the vehicle all the time in a first preset time period after the target time;
if yes, the interactive terminal sends out a first prompt tone.
7. The intelligent traffic management method based on the internet of things of claim 6, wherein the interactive terminal has a sound acquisition function and a speed acquisition function; the interactive terminal comprises an angle sensor for monitoring the rotation angle of a steering wheel of the vehicle and a grip strength sensor arranged on the steering wheel of the vehicle; the method further comprises the following steps:
the interactive terminal obtains the current time and generates a time factor Y based on the current timetWherein the time factor Y is set when the current time falls within a preset time intervalt1, when the current time does not fall into the preset time interval, the time factor YtTaking 0;
the interactive terminal carries out image analysis on the driver image in a second preset time period before the current time so as to obtain the head displacement S of the driver in the second preset time period before the current timetAnd generating a standard head displacement S based on the second preset time periodb；
The interactive terminal obtains the accumulated rotation angle J of the steering wheel in the second preset time period before the current moment of the vehicle through the angle sensorlAnd generating a standard rotation angle J based on the second preset time periodb；
The interactive terminal obtains the times C that the grip strength of the driver holding the steering wheel is smaller than the preset grip strength in the second preset time period before the current moment of the vehicle through the grip strength sensorw；
The interactive terminal carries out image analysis on the driver image in the second preset time period before the current time so as to obtain the yawning times C of the driver in the second preset time period before the current timeh；
The interactive terminal collects images of the copiers in the vehicle in real time and marks the images as copiers images;
the interactive terminal carries out image analysis on the copilot image in the second preset time period before the current moment so as to judge whether a copilot person exists in the vehicle in the second preset time period before the current moment;
if so, the interactionThe terminal generates a secondary driving constant A, the secondary driving constant A is 0, sound collection is carried out on a secondary driving area in the vehicle, and the frequency C of the sounds of secondary driving personnel in the vehicle in the second preset time period before the current moment is judgedyAnd generating a first standard time C based on the second preset time period1；
If not, the interactive terminal generates a secondary driving constant A, and the secondary driving constant A is 1;
the interactive terminal acquires road condition images in front of the vehicle in real time and analyzes the running speed of the vehicle in real time so as to judge whether unnecessary braking of the vehicle occurs in the second preset time period before the current moment;
if so, the interactive terminal generates a brake constant B, and the brake constant B is 10;
if not, the interactive terminal generates a brake constant B, and the brake constant B is 0;
the interactive terminal generates a fatigue driving indicated value Z:
Figure FDA0003187468820000051
the larger the fatigue driving indication value Z is, the larger the probability that the driver is fatigue driving is;
and when the fatigue driving indicated value is larger than a second preset value, the interactive terminal sends out a second prompt tone.
8. The intelligent traffic management method based on the internet of things as claimed in claim 1, further comprising:
the interactive terminal acquires the real-time distance between the vehicle and the nearest front vehicle;
the interactive terminal acquires a safety distance value corresponding to the vehicle;
and when the real-time distance is smaller than the safe distance value, the interactive terminal sends out a third prompt tone.
9. The intelligent traffic management method based on the internet of things of claim 8, wherein the interactive terminal has an image acquisition function, a sound acquisition function and a speed acquisition function; the interactive terminal obtains the safe distance value corresponding to the vehicle, and the method comprises the following steps:
the interactive terminal collects images of a driver in the vehicle in real time and marks the images as images of the driver;
the interactive terminal carries out image analysis on the driver image in a third preset time period before the current time so as to obtain the number C of times that the driver watches the mobile phone in the third preset time period before the current timekAnd generating a second standard time C based on the third preset time period2；
The interactive terminal acquires the times C of traffic accidents occurring in the third preset time period before the current moment of the vehicle through the cloud server and the traffic management big datasAnd generating a third standard time C based on the third preset time period3；
The interactive terminal analyzes the running speed of the vehicle in the third preset time period before the current moment so as to obtain the total times C of sudden braking and sudden acceleration of the vehiclezAnd generating a fourth standard time C based on the third preset time period4；
The interactive terminal obtains the average value t of the time interval from the time when the vehicle lights a brake tail lamp in the nearest preceding vehicle to the time when the vehicle has a sudden speed drop in the third preset time period before the current time through the image acquisition function and the speed acquisition functionjAnd obtaining a standard average value tb；
The interactive terminal generates a safe distance value D corresponding to the cost vehicle:
Figure FDA0003187468820000061
wherein D is1Is a standard distance value in meters, and is constant.
10. An intelligent traffic management system based on the internet of things is characterized by being applied to the intelligent traffic management method based on the internet of things according to any one of claims 1-9; the system comprises a cloud server, interactive terminals arranged in a vehicle and cameras arranged on all road sections; the interactive terminal and the camera are in communication connection with the cloud server.
CN202110865874.3A 2021-07-29 2021-07-29 Intelligent traffic management method and system based on Internet of things Withdrawn CN113593231A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114407778A (en) * 2022-02-25 2022-04-29 深圳市超越智能电子有限公司 Automobile 360-degree blind area video processing system and method
CN116972749A (en) * 2023-07-31 2023-10-31 神思电子技术股份有限公司 Facility positioning method, equipment and medium based on visual difference

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114407778A (en) * 2022-02-25 2022-04-29 深圳市超越智能电子有限公司 Automobile 360-degree blind area video processing system and method
CN116972749A (en) * 2023-07-31 2023-10-31 神思电子技术股份有限公司 Facility positioning method, equipment and medium based on visual difference

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