CN114241761A - Wisdom traffic signal lamp network deployment is optimization control system in coordination - Google Patents

Wisdom traffic signal lamp network deployment is optimization control system in coordination Download PDF

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Publication number
CN114241761A
CN114241761A CN202111527586.3A CN202111527586A CN114241761A CN 114241761 A CN114241761 A CN 114241761A CN 202111527586 A CN202111527586 A CN 202111527586A CN 114241761 A CN114241761 A CN 114241761A
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traffic
signal lamp
data
module
unit
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CN114241761B (en
Inventor
陈嘉鹏
黎忠华
陈春英
陈乙利
高波
李有朋
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Shenzhen Pengcheng Transportation Network Co ltd
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Shenzhen Pengcheng Transportation Network 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • 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
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B3/00—Audible signalling systems; Audible personal calling systems
    • G08B3/10—Audible signalling systems; Audible personal calling systems using electric transmission; using electromagnetic transmission
    • 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
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/081—Plural intersections under common control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/095—Traffic lights

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

Abstract

The invention provides a networking cooperative optimization control system for intelligent traffic signal lamps, which comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring traffic flow data of a plurality of traffic intersections; the data processing module is used for processing the traffic flow data to obtain integrated information; the instruction output module is used for outputting a corresponding signal lamp instruction according to the integrated information; the signal lamp control module is used for controlling the switching of the signal lamps according to the signal lamp instructions; the traffic signal lamp control system analyzes the traffic flow conditions of all traffic intersections and cooperatively controls the traffic signal lamps of adjacent traffic intersections according to the analysis result, so that the traffic signal lamps can intelligently adjust the change of the traffic signal lamps according to the traffic conditions of the adjacent intersections, the intellectualization of the traffic signal lamps is realized, the urban traffic problem is optimized, and the traffic burden is reduced.

Description

Wisdom traffic signal lamp network deployment is optimization control system in coordination
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal lamp networking collaborative optimization control system.
Background
Along with the increase of urban scale and the rapid increase of urban quantity, urban traffic problems are gradually revealed, especially, traffic conditions in rush hours in the morning and evening become one of important problems in urban development, and in order to reduce urban traffic burden and promote urban healthy development, an intelligent traffic signal lamp networking cooperative optimization control system is urgently needed, so that the intelligentization of traffic signal lamps at traffic intersections is realized, the urban traffic problems are optimized, the urban traffic burden is reduced, and the urban healthy development is promoted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the intelligent traffic signal lamp networking cooperative optimization control system, which analyzes the traffic flow conditions of each traffic intersection and cooperatively controls the traffic signal lamps of adjacent traffic intersections according to the analysis result, so that the traffic signal lamps can intelligently adjust the traffic light change of the traffic signal lamps according to the traffic conditions of the adjacent intersections, the intellectualization of the traffic signal lamps is realized, the urban traffic problem is optimized, and the traffic burden is reduced.
A wisdom traffic signal lamp networking is optimization control system in coordination includes:
the data acquisition module is used for acquiring traffic flow data of a plurality of traffic intersections;
the data processing module is used for processing the traffic flow data to obtain integrated information;
the instruction output module is used for outputting a corresponding signal lamp instruction according to the integrated information;
and the signal lamp control module is used for controlling the switching of the signal lamps according to the signal lamp instructions.
As an embodiment of the invention, the data acquisition module comprises a plurality of traffic camera units and a data sending unit;
each traffic camera unit corresponds to a camera area of one traffic intersection and has a unique number;
the traffic camera shooting unit is used for collecting vehicle data in a corresponding camera shooting area;
the data sending unit is used for integrating the vehicle data into traffic flow data and sending the traffic flow data to the data processing module; the traffic data comprises a number corresponding to the traffic camera shooting unit, vehicle data shot by the traffic camera shooting unit and time shot by the traffic camera shooting unit.
As an embodiment of the present invention, a data processing module includes:
the data receiving unit is used for receiving the traffic data sent by the data acquisition module;
the data analysis and calculation unit is used for analyzing the number of the vehicles staying in the traffic flow data when the red light is in each camera area and calculating the vehicle staying time length when the red light is in each camera area in the traffic flow data;
the data joint calculation unit is used for calculating the average passing time required by the vehicle to pass through the intersection according to the vehicle number plate information and the vehicle staying time in different camera shooting areas in the traffic flow data;
and the data integration and transmission unit is used for integrating the number of the vehicles staying and the average passing time to obtain integration information and transmitting the integration information to the instruction output module.
As an embodiment of the present invention, the instruction output module includes:
the information receiving unit is used for receiving the integration information sent by the data processing module;
the information grouping unit is used for grouping the integrated information to obtain a plurality of groups of independent information; each group of independent information comprises integrated information in two adjacent camera shooting areas;
the information judgment unit is used for judging whether the traffic signal lamp needs to be changed or not according to the independent information based on the trained and re-excited learned AI body, and generating a judgment result;
and the result execution unit is used for generating a corresponding signal lamp instruction according to the judgment result and sending the signal lamp instruction to the signal lamp control module.
As an embodiment of the invention, the information judging unit executes the following operations:
acquiring independent information, and monitoring the traffic condition of adjacent camera shooting areas according to the independent information based on the trained and re-excited learned AI body;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is poor is monitored, the traffic signal lamp is judged to need to be changed, and a judgment result of reducing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is poor is monitored, the traffic signal lamp is judged to be required to be changed, and a judgment result of increasing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is good, the traffic signal lamp is judged not to be required to be changed, and a judgment result that the traffic signal lamp is not required to be changed is generated.
As an embodiment of the present invention, if the determination result is that no change needs to be made to the traffic signal lamp, the signal lamp instruction generated by the result execution unit is none and is not sent to the signal lamp control module.
As an embodiment of the present invention, an intelligent traffic signal lamp networking cooperative optimization control system further includes: an auxiliary control module;
the auxiliary control module is used for receiving and displaying the integration information sent by the data processing module, the signal lamp instruction sent by the intervention instruction output module and directly controlling the signal lamp control module;
the auxiliary control module includes:
the receiving and displaying unit is used for receiving the integration information sent by the data processing module and displaying the integration information to workers;
the instruction intervention unit is used for sending an intervention instruction to interfere the signal lamp instruction sent by the instruction output module so as to enable the signal lamp instruction to be invalid;
and the direct control unit is used for sending a new signal lamp instruction to the signal lamp control module after the signal lamp instruction fails.
As an embodiment of the present invention, an intelligent traffic signal lamp networking cooperative optimization control system further includes: the intelligent prompt module is used for fast passing;
the quick passing intelligent reminding module executes the following operations:
during the waiting period of the red light, acquiring traffic flow images shot by a traffic camera unit, and carrying out image acquisition processing on the heads of drivers in the traffic flow images to obtain head images of a plurality of drivers;
screening the head images to obtain head images to be detected of drivers with the vehicle sequences at least arranged in the first three positions in the vehicle flow images;
inputting the head image to be detected into a human face orientation classification model which is trained in advance for processing to obtain the human face orientation of each driver in the head image to be detected;
judging whether the face orientation of each driver in the head image to be detected points to the crossing traffic direction or not to obtain a first judgment result;
acquiring the residual red light time of the traffic signal lamp, and judging whether the residual red light time is less than the preset starting time to obtain a second judgment result;
and when the second judgment result is that the remaining red light time is less than the preset starting time and the first judgment result is that the face of any driver in the head image to be detected faces the direction which does not point to the crossing traffic direction, sending out a rapid traffic voice prompt.
As an embodiment of the present invention, the fast passing intelligent reminding module further includes: a fatigue driving monitoring module;
the fatigue driving monitoring module executes the following operations:
during the waiting period of the red light, a second traffic image pickup unit with high resolution is arranged in advance to pick up the traffic information to obtain a second traffic image;
the method comprises the steps that a traffic flow image shot by a traffic camera unit is obtained, and when a fast passing intelligent reminding module carries out fast passing voice reminding and a traffic signal lamp is turned into a green light, the first average passing time of a vehicle driven by a driver with face orientation classification passing through a current traffic intersection is calculated;
calculating a second average passing time length when other vehicles pass through the current traffic intersection except the vehicle driven by the driver with the face orientation classification before the current traffic signal lamp is converted into the red light;
if the difference value between the first average elapsed time length and the second average elapsed time length is greater than the preset reaction difference value;
carrying out image acquisition processing on the eyes of the driver subjected to face orientation classification in the second traffic image to obtain an eye image;
inputting the eye image into an eye focusing degree pre-estimation model which is trained in advance to be processed, and obtaining the focusing degree value of the eyes of the current driver in the process of waiting for the red light to pass through the green light;
judging whether the focusing degree value of eyes of a driver is lower than a preset focusing degree threshold value or not in the process of waiting for a red light to pass a green light at present;
if so, judging that the current driver has suspicion of fatigue driving, and recording the vehicle number plate of the current driver with suspicion of fatigue driving to obtain a fatigue vehicle number plate;
acquiring traffic flow images shot by all traffic camera units, and acquiring a second eye image of a driver of a fatigue vehicle shot by a second traffic camera unit corresponding to a traffic intersection if the fatigue vehicle with the same vehicle number plate as the fatigue vehicle number plate appears in the traffic flow images;
inputting the second eye image into a pre-trained eye focusing degree estimation model for processing to obtain a second focusing degree value of eyes of a current driver in the process of waiting for a red light to pass a green light;
judging whether a second focusing degree value of eyes exists at a moment when the current driver waits for the red light to pass through the green light and is lower than a preset focusing degree threshold value or not;
if the driver fatigue driving warning device exists, the driver fatigue driving warning device sends out a driver fatigue driving voice prompt by combining with a fatigue vehicle number plate, and sends a monitoring result to a background terminal for early warning.
As an embodiment of the present invention, an intelligent traffic signal lamp networking cooperative optimization control system further includes: a traffic light change advanced planning module;
the traffic light change advanced planning module executes the following operations:
acquiring daily traffic flow data of a plurality of traffic intersections through a data acquisition module;
determining the peak time of the traffic flow at each traffic intersection according to the daily traffic flow data, and determining the peak traveling direction of the peak traffic flow at the peak time of the traffic flow at each traffic intersection;
determining the idle traveling direction of idle traffic flow which does not belong to the traffic flow peak at each traffic intersection in the traffic flow peak period and the idle average number of idle traffic flow which belongs to the idle traveling direction during each red light waiting period according to daily traffic flow data;
acquiring the average duration of the idle traffic flows passing through the traffic intersection and the supplement time required for the number of the idle traffic flows in the next idle traveling direction to reach the idle average number again;
determining the idle passing time of the idle traffic flow according to the average idle number and the average time;
presetting a pedestrian crossing induction button at each traffic intersection;
triggering a pedestrian street-crossing sensing button when a pedestrian wants to pass through the zebra crossing, wherein the pedestrian street-crossing sensing button acquires preset waiting time, and the zebra crossing traffic signal lamp is converted into a green lamp after the preset waiting time;
adjusting the peak red light time length in the peak traveling direction according to the idle passing time length and the preset green light time length after the pedestrian crossing sensing button is triggered;
adjusting the peak green light time length of the peak traveling direction according to the supplement time length and the waiting time length before the zebra crossing traffic signal lamp is turned into the green light after the preset pedestrian crossing induction button is triggered;
adjusting the idle green light duration of the idle traveling direction according to the idle passing duration and the waiting duration before the zebra crossing traffic signal lamp is turned to green light after the preset pedestrian crossing induction button is triggered;
and adjusting the idle red light duration of the idle advancing direction according to the supplement duration and the green light duration after the preset pedestrian street crossing induction button is triggered.
The invention has the beneficial effects that:
the traffic signal lamps of the adjacent traffic intersections are cooperatively controlled according to the analysis result by analyzing the traffic flow conditions of the traffic intersections, so that the traffic signal lamps can intelligently adjust the change of the traffic signals according to the traffic conditions of the adjacent intersections, the intellectualization of the traffic signal lamps is realized, the urban traffic problem is optimized, and the traffic burden is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic system diagram 1 of an intelligent traffic signal lamp networking cooperative optimization control system in an embodiment of the present invention;
fig. 2 is a schematic diagram of a data acquisition module in a smart traffic signal networking cooperative optimization control system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a data processing module in a smart traffic signal networking cooperative optimization control system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an instruction output module in a smart traffic signal lamp networking cooperative optimization control system according to an embodiment of the present invention;
fig. 5 is a schematic system diagram of an intelligent traffic signal lamp networking cooperative optimization control system in an embodiment of the present invention 2;
fig. 6 is a schematic diagram of an auxiliary control module in a smart traffic signal networking cooperative optimization control system according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a fast-passing intelligent reminding module in a collaborative optimization control system for intelligent traffic signal lamp networking according to an embodiment of the present invention;
fig. 8 is a schematic flowchart illustrating a training procedure of a face orientation classification model in a collaborative optimization control system for intelligent traffic signal networking according to an embodiment of the present invention;
fig. 9 is a schematic flowchart of training steps of an eye focus degree estimation model in a collaborative optimization control system for intelligent traffic signal lamp networking according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1, an embodiment of the present invention provides an intelligent traffic signal lamp networking cooperative optimization control system, including:
the data acquisition module is used for acquiring traffic flow data of a plurality of traffic intersections;
the data processing module is used for processing the traffic flow data to obtain integrated information;
the instruction output module is used for outputting a corresponding signal lamp instruction according to the integrated information;
the signal lamp control module is used for controlling the switching of the signal lamps according to the signal lamp instructions;
the working principle of the technical scheme is as follows: the data acquisition module controls the traffic camera units to acquire traffic data of a plurality of traffic intersections and sends the traffic data to the data processing module for processing, wherein each traffic intersection is provided with a traffic camera unit, and each traffic camera unit preferably comprises a monitoring camera corresponding to the traffic intersection, so that the cost is saved; the data processing module integrates the traffic flow data to obtain integrated information and sends the integrated information to the instruction output module, wherein the integrated information preferably comprises the number of vehicles staying in the traffic flow data, the staying time of the vehicles and the average passing time required for the vehicles to pass through the corresponding intersections; the instruction output module is used for outputting corresponding signal lamp instructions according to the integrated information, wherein the signal lamp instructions preferably comprise a change instruction of the traffic signal lamp and the position of a traffic intersection corresponding to the traffic signal lamp to be changed; the instruction output module carries out networking control on the traffic signal lamps of a plurality of traffic intersections, namely the signal lamp instruction of the current traffic intersection is influenced by the traffic conditions of the adjacent traffic intersections, so that the traffic condition of each traffic intersection can be cooperatively adjusted according to the traffic conditions of the adjacent traffic intersections, the traffic efficiency is improved, and the traffic burden is reduced; the signal lamp control module receives the signal lamp instruction sent by the instruction output module to control the signal lamp of the corresponding traffic intersection to change;
the beneficial effects of the above technical scheme are: the system integrates and analyzes the traffic flow conditions of each traffic intersection, and cooperatively controls the traffic signal lamps of adjacent traffic intersections according to the analysis result, so that the traffic signal lamps can intelligently adjust the change of the traffic signals according to the traffic conditions of the adjacent intersections, the intellectualization of the traffic signal lamps is realized, the urban traffic problem is optimized, and the traffic burden is reduced.
Referring to fig. 2, in an embodiment, the data acquisition module includes a plurality of traffic camera units and a data transmission unit;
each traffic camera unit corresponds to a camera area of one traffic intersection and has a unique number;
the traffic camera shooting unit is used for collecting vehicle data in a corresponding camera shooting area;
the data sending unit is used for integrating the vehicle data into traffic flow data and sending the traffic flow data to the data processing module; the traffic flow data comprises a number corresponding to the traffic camera shooting unit, vehicle data shot by the traffic camera shooting unit and time shot by the traffic camera shooting unit;
the working principle and the beneficial effects of the technical scheme are as follows: each traffic camera unit corresponds to the camera area of one traffic intersection and has a unique number, and the traffic camera unit preferably comprises a monitoring camera corresponding to the traffic intersection and a preset image processing subunit, so that the cost is saved; the traffic camera shooting unit collects vehicle data in a corresponding camera shooting area; the vehicle data is acquired from the traffic flow image shot by the traffic camera unit, and the vehicle data comprises but is not limited to the number of the stopped vehicles, the number plate information of the stopped vehicles and the like; the data sending unit integrates the vehicle data into traffic data and sends the traffic data to the data processing module; the traffic flow data comprises a number corresponding to the traffic camera shooting unit, vehicle data shot by the traffic camera shooting unit and time shot by the traffic camera shooting unit; the number corresponding to the traffic camera shooting unit carried in the traffic data and the camera shooting time of the traffic camera shooting unit are beneficial to quickly determining the shooting place and the shooting time of each traffic data, and the inquiry is facilitated.
Referring to fig. 3, in one embodiment, the data processing module includes:
the data receiving unit is used for receiving the traffic data sent by the data acquisition module;
the data analysis and calculation unit is used for analyzing the number of the vehicles staying in the traffic flow data when the red light is in each camera area and calculating the vehicle staying time length when the red light is in each camera area in the traffic flow data;
the data joint calculation unit is used for calculating the average passing time required by the vehicle to pass through the intersection according to the vehicle number plate information and the vehicle staying time in different camera shooting areas in the traffic flow data;
the data integration and transmission unit is used for integrating the number of the vehicles staying and the average passing time to obtain integration information and transmitting the integration information to the instruction output module;
the working principle of the technical scheme is as follows: the data processing module is used for handling the traffic flow data, obtains the integration information and sends to instruction output module, and the data processing module includes: the device comprises a data receiving unit, a data analysis and calculation unit, a data joint calculation unit and a data integration and transmission unit; the data receiving unit receives traffic data sent by the data acquisition module; the data analysis and calculation unit is used for analyzing the number of the vehicles staying at the red light in each camera area in the traffic flow data, calculating the vehicle staying time length at the red light in each camera area in the traffic flow data, and further analyzing and calculating the total length of the waiting traffic flow at the red light; the data joint calculation unit calculates the average passing time required by the vehicle to pass through the intersection according to the vehicle number plate information and the vehicle staying time in different camera shooting areas in the traffic flow data, wherein the average passing time comprises the waiting time of the vehicle at the intersection and the time of the vehicle passing through the intersection; furthermore, when a certain vehicle is far away from the traffic camera unit, the data combination calculation unit cannot determine the number plate information of the vehicle through the traffic flow data, randomly generates a unique number for the vehicle to represent the number plate information of the vehicle through identifying the characteristics of the vehicle, and acquires the number plate information to replace the generated unique number for participating in calculation after the data combination calculation unit can determine the number plate information of the vehicle from the traffic flow data; the data integration and transmission unit integrates the vehicle stopping number and the average passing time to obtain integration information and transmits the integration information to the instruction output module; furthermore, the total length of the waiting traffic flow during the red light is integrated into the integrated information and sent to the instruction output module to participate in the subsequent instruction output judgment, so that the judgment accuracy is improved;
the beneficial effects of the above technical scheme are: the collected data are analyzed, calculated and integrated, and the accuracy of subsequent instruction judgment is improved beneficially.
Referring to fig. 4, in one embodiment, the instruction output module includes:
the information receiving unit is used for receiving the integration information sent by the data processing module;
the information grouping unit is used for grouping the integrated information to obtain a plurality of groups of independent information; each group of independent information comprises integrated information in two adjacent camera shooting areas;
the information judgment unit is used for judging whether the traffic signal lamp needs to be changed or not according to the independent information based on the trained and re-excited learned AI body, and generating a judgment result;
the result execution unit is used for generating a corresponding signal lamp instruction according to the judgment result and sending the signal lamp instruction to the signal lamp control module;
the information judgment unit executes the following operations:
acquiring independent information, and monitoring the traffic condition of adjacent camera shooting areas according to the independent information based on the trained and re-excited learned AI body;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is poor is monitored, the traffic signal lamp is judged to need to be changed, and a judgment result of reducing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is poor is monitored, the traffic signal lamp is judged to be required to be changed, and a judgment result of increasing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is good, judging that the traffic signal lamp does not need to be changed, and generating a judgment result that the traffic signal lamp does not need to be changed;
furthermore, if the judgment result is that the traffic signal lamp does not need to be changed, the signal lamp instruction generated by the result execution unit is absent and is not sent to the signal lamp control module;
the working principle of the technical scheme is as follows: the instruction output module is used for outputting a corresponding signal lamp instruction according to the integrated information; the instruction output module comprises an information receiving unit, an information grouping unit, an information judging unit and a result executing unitThe information receiving unit receives the integration information sent by the data processing module; the information grouping unit groups the integrated information to obtain a plurality of groups of independent information; each group of independent information comprises integrated information in two adjacent camera shooting areas, and the two adjacent camera shooting areas preferably refer to a current camera shooting area and a next camera shooting area, namely a current traffic intersection and a next traffic intersection through which vehicles need to pass after passing through the current traffic intersection; the information judgment unit judges whether the traffic signal lamp needs to be changed or not according to the independent information based on the trained and re-excited learned AI body, and generates a judgment result; the method specifically comprises the following steps: acquiring independent information, and monitoring the traffic condition of adjacent camera areas according to the independent information based on an AI (intellectual insight) body of the re-excitation learning after training; if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is poor is monitored, the traffic signal lamp is judged to need to be changed, and a judgment result of reducing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated; if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is poor is monitored, the traffic signal lamp is judged to be required to be changed, and a judgment result of increasing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated; if the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is good, judging that the traffic signal lamp does not need to be changed, and generating a judgment result that the traffic signal lamp does not need to be changed; further, the training step of the re-excitation learning of the ai (intellectual intelligence) body includes: step 1: acquiring adjacent traffic intersection integration information calculation index values, wherein the calculation method of the index values comprises the following steps: index ═ x [ [ (x)1-x2*γ),(δ)](ii) a Wherein index is index value, x1Number of vehicles stopped at current traffic intersection, x2The number of vehicles staying at the same-direction adjacent traffic intersection is determined, gamma is a weight coefficient, and gamma is [0,1 ═]δ is the average passing time length, and γ is 0 when index is a negative number; step 2: setting an excitation function of an AI (Artificial Intelligence) body, wherein the calculation method of the excitation function is as follows: f ═ x1-x2γ + δ); it is composed ofIn, f is the excitation function; and step 3: training an AI (Artificial Intelligence) body on a simulation platform by re-excitation learning according to the index value obtained in the step 1 and the excitation function obtained in the step 2; the integrated information of the adjacent traffic intersections is adopted to carry out re-excitation learning training on the AI (Artificial Intelligence) body, which is beneficial to improving the judgment accuracy of the AI (Artificial Intelligence) body and improving the control efficiency of the traffic signal lamp; furthermore, if the judgment result is that the traffic signal lamp does not need to be changed, the signal lamp instruction generated by the result execution unit is absent and is not sent to the signal lamp control module;
the beneficial effects of the above technical scheme are: whether the traffic signal lamp of the current traffic intersection needs to be adjusted or not is cooperatively judged according to the integrated information of the adjacent traffic intersections, so that the traffic efficiency of the urban intersections is integrally improved, and the traffic burden is reduced.
Referring to fig. 5 and fig. 6, in an embodiment, an intelligent traffic signal networking cooperative optimization control system further includes: an auxiliary control module;
the auxiliary control module is used for receiving and displaying the integration information sent by the data processing module, the signal lamp instruction sent by the intervention instruction output module and directly controlling the signal lamp control module;
the auxiliary control module includes:
the receiving and displaying unit is used for receiving the integration information sent by the data processing module and displaying the integration information to workers;
the instruction intervention unit is used for sending an intervention instruction to interfere the signal lamp instruction sent by the instruction output module so as to enable the signal lamp instruction to be invalid;
the direct control unit is used for sending a new signal lamp instruction to the signal lamp control module after the signal lamp instruction fails;
the working principle and the beneficial effects of the technical scheme are as follows: the auxiliary control module is used for receiving and displaying the integration information sent by the data processing module, the signal lamp instruction sent by the intervention instruction output module and directly controlling the signal lamp control module, wherein the auxiliary control module comprises a receiving display unit, an instruction intervention unit and a direct control unit; the receiving and displaying unit receives the integrated information sent by the data processing module and displays the integrated information to the staff, so that the staff can directly check the integrated information at a background terminal, and the processing is convenient for the staff; the instruction intervention unit sends an intervention instruction to interfere a signal lamp instruction sent by the instruction output module to cause the signal lamp instruction to be invalid, when a worker wants to modify an instruction sent after the current AI (Artificial Intelligence) body is judged, the instruction intervention unit can realize the intervention, which is beneficial to strengthening the overall control of the worker on the system and preventing traffic paralysis caused by AI (Artificial Intelligence) body judgment logic errors due to the invasion of the system; the direct control unit is used for sending a new signal lamp instruction to the signal lamp control module after the signal lamp instruction fails, so that a worker can skip an AI (Artificial Intelligence) body to directly adjust the traffic signal lamp, and the system is beneficial to strengthening the overall control of the worker on the system.
Referring to fig. 5 and 8, in an embodiment, an intelligent traffic signal networking cooperative optimization control system further includes: the intelligent prompt module is used for fast passing;
the quick passing intelligent reminding module executes the following operations:
during the waiting period of the red light, acquiring traffic flow images shot by a traffic camera unit, and carrying out image acquisition processing on the heads of drivers in the traffic flow images to obtain head images of a plurality of drivers;
screening the head images to obtain head images to be detected of drivers with the vehicle sequences at least arranged in the first three positions in the vehicle flow images;
inputting the head image to be detected into a human face orientation classification model which is trained in advance for processing to obtain the human face orientation of each driver in the head image to be detected;
judging whether the face orientation of each driver in the head image to be detected points to the crossing traffic direction or not to obtain a first judgment result;
acquiring the residual red light time of the traffic signal lamp, and judging whether the residual red light time is less than the preset starting time to obtain a second judgment result;
when the second judgment result is that the remaining red light time is less than the preset starting time and the first judgment result is that the face of any driver in the head image to be detected faces towards the direction which does not point to the crossing traffic direction, a rapid traffic voice prompt is sent;
the working principle of the technical scheme is as follows: the driver often does other things after braking in the process of waiting for the red light, such as looking at, chatting and the like, so that the driver who is arranged in front of the waiting sequence after the red light is finished cannot start the automobile to drive away in the first time, and the passing vehicles in the same green light time are fewer, so that the waiting vehicle accumulation is generated, particularly the situation of waiting vehicle accumulation in the urban peak time period is more serious, the state of the driver who is arranged in front of the waiting sequence is judged before the red light is finished through the quick passing intelligent reminding module, and when the state of the driver who is arranged in front of the waiting sequence cannot pay attention to the traffic light information in time, the intelligent voice reminding module is sent out to remind the driver to make preparation for driving away as soon as possible, which is beneficial to improving the speed of the vehicle passing through a traffic intersection and preventing traffic jam caused by over-slow passing speed;
the quick passing intelligent reminding module executes the following operations: during the waiting period of the red light, acquiring traffic flow images shot by a traffic camera unit, and carrying out image acquisition processing on the heads of drivers in the traffic flow images to obtain head images of a plurality of drivers; screening the head images to obtain head images to be detected of drivers with the vehicle sequences at least arranged in the first three positions in the vehicle flow images; inputting the head image to be detected into a human face orientation classification model which is trained in advance for processing to obtain the human face orientation of each driver in the head image to be detected; preferably, the training step of the face orientation classification model is S1, a plurality of face orientation sample data are obtained, the face orientation sample data are divided according to a preset proportion, and a plurality of training sample data and a plurality of verification sample data are obtained; s2, performing artificial face orientation labeling processing on the training sample data and the verification sample data respectively to obtain labeled training sample data and labeled verification sample data; s3, obtaining an initial face orientation classification model, inputting a plurality of labeled training sample data into the initial face orientation classification model for training, and obtaining a face orientation classification model; wherein, the initial face orientation classification model is preferably a machine learning model; s4, inputting a plurality of labeled verification sample data into the face orientation classification model to carry out face orientation accuracy verification to obtain a verification result, if the verification result does not pass, executing S1 in a preset proportion again, and if the verification passes, determining the face orientation classification model; judging whether the face orientation of each driver in the head image to be detected points to the crossing traffic direction or not to obtain a first judgment result; the range of the crossing traffic direction is preferably the range of the traffic signal lamp which deviates 25 degrees from the left and the right; acquiring the residual red light time of the traffic signal lamp, and judging whether the residual red light time is less than the preset starting time to obtain a second judgment result; the preset starting time is preferably 10s, and when the second judgment result shows that the remaining red light time is less than the preset starting time and the first judgment result shows that the face of any driver in the head image to be detected faces the direction which does not point to the crossing traffic direction, a quick traffic voice prompt is sent out;
the beneficial effects of the above technical scheme are: through the quick passing intelligent reminding module, the state of the driver in the front of the waiting sequence is judged before the red light is finished, when the state of the driver in the front of the waiting sequence cannot pay attention to the traffic light information in time, the intelligent voice reminding module sends out an intelligent voice reminding to remind the driver to make preparations for driving away as soon as possible, so that the speed of the vehicle passing through a traffic intersection is improved, traffic jam caused by too low passing speed is prevented, and the urban traffic problem is optimized.
Referring to fig. 7 and 9, in an embodiment, the quick passing intelligent reminding module further includes: a fatigue driving monitoring module;
the fatigue driving monitoring module executes the following operations:
during the waiting period of the red light, a second traffic image pickup unit with high resolution is arranged in advance to pick up the traffic information to obtain a second traffic image;
the method comprises the steps that a traffic flow image shot by a traffic camera unit is obtained, and when a fast passing intelligent reminding module carries out fast passing voice reminding and a traffic signal lamp is turned into a green light, the first average passing time of a vehicle driven by a driver with face orientation classification passing through a current traffic intersection is calculated;
calculating a second average passing time length when other vehicles pass through the current traffic intersection except the vehicle driven by the driver with the face orientation classification before the current traffic signal lamp is converted into the red light;
if the difference value between the first average elapsed time length and the second average elapsed time length is greater than the preset reaction difference value;
carrying out image acquisition processing on the eyes of the driver subjected to face orientation classification in the second traffic image to obtain an eye image;
inputting the eye image into an eye focusing degree pre-estimation model which is trained in advance to be processed, and obtaining the focusing degree value of the eyes of the current driver in the process of waiting for the red light to pass through the green light;
judging whether the focusing degree value of eyes of a driver is lower than a preset focusing degree threshold value or not in the process of waiting for a red light to pass a green light at present;
if so, judging that the current driver has suspicion of fatigue driving, and recording the vehicle number plate of the current driver with suspicion of fatigue driving to obtain a fatigue vehicle number plate;
acquiring traffic flow images shot by all traffic camera units, and acquiring a second eye image of a driver of a fatigue vehicle shot by a second traffic camera unit corresponding to a traffic intersection if the fatigue vehicle with the same vehicle number plate as the fatigue vehicle number plate appears in the traffic flow images;
inputting the second eye image into a pre-trained eye focusing degree estimation model for processing to obtain a second focusing degree value of eyes of a current driver in the process of waiting for a red light to pass a green light;
judging whether a second focusing degree value of eyes exists at a moment when the current driver waits for the red light to pass through the green light and is lower than a preset focusing degree threshold value or not;
if the vehicle number plate exists, sending out a fatigue driving voice prompt by combining with the fatigue vehicle number plate, and sending a monitoring result to a background terminal for early warning;
the working principle of the technical scheme is as follows: the phenomenon of car accidents caused by fatigue driving occurs every year, but the detection of the fatigue driving is only a few detection modes such as traffic police patrol and inspection and the like, and the omission condition is easily caused when the fatigue driving is detected by the traffic police patrol and inspection mode, but the driver in the fatigue state always selects to temporarily rest on a steering wheel for a period of time or fall into a failure state during the waiting period of a red light, so as to achieve the purpose of short rest, the driver in the fatigue driving is easily prone to fall into a sleep state, but the focusing degree of eyes changes when the driver is in the process of changing from the sleep state to a clear state, the fatigue driving monitoring module is used for monitoring the fatigue driving of the driver waiting for a traffic light at a traffic intersection, when the driver is monitored to be in the fatigue driving state, a prompt is sent and data is transmitted to a background terminal, and if necessary, the driver in the traffic background terminal contacts a police for intercepting, accidents of drivers caused by fatigue driving are prevented, and the urban traffic safety is improved;
the fatigue driving monitoring module executes the following operations: during the waiting period of the red light, a second traffic image pickup unit with high resolution is arranged in advance to pick up the traffic information to obtain a second traffic image; if the resolution of the monitoring camera at the traffic intersection is enough, the traffic camera unit and the second traffic camera unit are preferably combined into the traffic camera unit, which is beneficial to saving the cost; the method comprises the steps that a traffic flow image shot by a traffic camera unit is obtained, and when a fast passing intelligent reminding module carries out fast passing voice reminding and a traffic signal lamp is turned into a green light, the first average passing time of a vehicle driven by a driver with face orientation classification passing through a current traffic intersection is calculated; the first average elapsed time length is the average passing time of the driver with the face orientation classification passing through the traffic intersection; calculating a second average passing time length when other vehicles pass through the current traffic intersection except the vehicle driven by the driver with the face orientation classification before the current traffic signal lamp is converted into the red light; if the difference value between the first average elapsed time length and the second average elapsed time length is greater than the preset reaction difference value; the preset reaction difference value is preferably the average reaction time of a normal person plus the average vehicle starting time of the normal person, and is preferably 1.25 s; carrying out image acquisition processing on the eyes of the driver subjected to face orientation classification in the second traffic image to obtain an eye image; inputting the eye image into an eye focusing degree pre-estimation model which is trained in advance to be processed, and obtaining the focusing degree value of the eyes of the current driver in the process of waiting for the red light to pass through the green light; the training process of the eye focusing degree estimation model preferably comprises the following steps: s11, obtaining a plurality of eye image sample data, and dividing the plurality of eye image sample data according to a preset proportion to obtain a plurality of second training sample data and a plurality of second verification sample data; wherein, the plurality of eye image sample data types comprise a plurality of eye image sample data with concentrated or scattered attention; s12, respectively carrying out manual eye focusing degree value labeling processing on the plurality of second training sample data and the plurality of second verification sample data to obtain a plurality of second labeled training sample data and a plurality of second labeled verification sample data; s13, obtaining an initial eye focusing degree pre-estimation model, inputting a plurality of second labeled training sample data into the initial eye focusing degree pre-estimation model for training, and obtaining an eye focusing degree pre-estimation model; the initial eye focusing degree estimation model is preferably a depth convolution neural network initial model; s14, inputting a plurality of second labeled verification sample data into the eye focusing degree pre-estimation model for face orientation accuracy verification to obtain a verification result, if the verification result does not pass, executing S11 in a preset proportion again, and if the verification passes, determining the eye focusing degree pre-estimation model; judging whether the focusing degree value of eyes of a driver is lower than a preset focusing degree threshold value or not in the process of waiting for a red light to pass a green light at present; the preset focusing degree threshold value is preferably a focusing degree value when normal people are absent; if so, judging that the current driver has suspicion of fatigue driving, and recording the vehicle number plate of the current driver with suspicion of fatigue driving to obtain a fatigue vehicle number plate; acquiring traffic flow images shot by all traffic camera units, and acquiring a second eye image of a driver of a fatigue vehicle shot by a second traffic camera unit corresponding to a traffic intersection if the fatigue vehicle with the same vehicle number plate as the fatigue vehicle number plate appears in the traffic flow images; inputting the second eye image into a pre-trained eye focusing degree estimation model for processing to obtain a second focusing degree value of eyes of a current driver in the process of waiting for a red light to pass a green light; judging whether a second focusing degree value of eyes exists at a moment when the current driver waits for the red light to pass through the green light and is lower than a preset focusing degree threshold value or not; if the vehicle number plate exists, sending out a fatigue driving voice prompt by combining with the fatigue vehicle number plate, and sending a monitoring result to a background terminal for early warning; furthermore, in order to save cost, the second traffic camera unit can be in an always-on mode, when the difference value between the first average passing time and the second average passing time is larger than a preset reaction difference value, the number plate information of the vehicle driven by the driver with face orientation classification is obtained and suspected to be labeled, when the traffic camera unit at a certain traffic intersection obtains the vehicle with the same number plate as the suspected to be labeled number plate information, the second traffic camera unit is started, if the current driver is suspected to be in fatigue driving, the subsequent process is executed, and if the current driver is not suspected to be in fatigue driving, the suspected label is removed from the number plate information of the vehicle corresponding to the driver;
the beneficial effects of the above technical scheme are: through driver fatigue monitoring module, carry out driver fatigue monitoring to the driver that waits for the traffic lights at traffic crossing department, when monitoring that there is the driver for the driver fatigue state, send and remind and with data transmission to backstage terminal, contact traffic police through the staff at backstage terminal and intercept as necessary, prevent that the driver from appearing the accident because of driver fatigue, improve city traffic safety.
In one embodiment, an intelligent traffic signal lamp networking cooperative optimization control system further includes: a traffic light change advanced planning module;
the traffic light change advanced planning module executes the following operations:
acquiring daily traffic flow data of a plurality of traffic intersections through a data acquisition module;
determining the peak time of the traffic flow at each traffic intersection according to the daily traffic flow data, and determining the peak traveling direction of the peak traffic flow at the peak time of the traffic flow at each traffic intersection;
determining the idle traveling direction of idle traffic flow which does not belong to the traffic flow peak at each traffic intersection in the traffic flow peak period and the idle average number of idle traffic flow which belongs to the idle traveling direction during each red light waiting period according to daily traffic flow data;
acquiring the average duration of the idle traffic flows passing through the traffic intersection and the supplement time required for the number of the idle traffic flows in the next idle traveling direction to reach the idle average number again;
determining the idle passing time of the idle traffic flow according to the average idle number and the average time;
presetting a pedestrian crossing induction button at each traffic intersection;
triggering a pedestrian street-crossing sensing button when a pedestrian wants to pass through the zebra crossing, wherein the pedestrian street-crossing sensing button acquires preset waiting time, and the zebra crossing traffic signal lamp is converted into a green lamp after the preset waiting time;
adjusting the peak red light time length in the peak traveling direction according to the idle passing time length and the preset green light time length after the pedestrian crossing sensing button is triggered;
adjusting the peak green light time length of the peak traveling direction according to the supplement time length and the waiting time length before the zebra crossing traffic signal lamp is turned into the green light after the preset pedestrian crossing induction button is triggered;
adjusting the idle green light duration of the idle traveling direction according to the idle passing duration and the waiting duration before the zebra crossing traffic signal lamp is turned to green light after the preset pedestrian crossing induction button is triggered;
adjusting the idle red light duration of the idle traveling direction according to the supplement duration and the preset green light duration after the pedestrian street crossing sensing button is triggered;
the working principle of the technical scheme is as follows: the urban traffic is regular, namely, the traffic flow peak always appears in the same time interval, but the advancing direction of the traffic intersection is multidirectional, so that some advancing directions of the same traffic intersection are in peak congestion, but some advancing directions are in idle states, but the time of the traffic signal lamp is fixed, or the traffic signal lamp is controlled to change cooperatively after congestion, so that the quality of urban traffic is greatly reduced, the data of the traffic signal lamp change advance planning module is used for collecting and analyzing the data of the peak time interval and the idle time interval of the traffic intersection in different advancing directions, the traffic signal lamp time of the traffic signal lamp of the traffic intersection is adjusted in advance according to the analysis result, and the planning is beneficial to improving the traffic efficiency compared with the planning after congestion;
the traffic light change advanced planning module executes the following operations: acquiring daily traffic flow data of a plurality of traffic intersections through a data acquisition module; determining the peak time of the traffic flow at each traffic intersection according to the daily traffic flow data, and determining the peak traveling direction of the peak traffic flow at the peak time of the traffic flow at each traffic intersection; determining the idle traveling direction of idle traffic flow which does not belong to the traffic flow peak at each traffic intersection in the traffic flow peak period and the idle average number of idle traffic flow which belongs to the idle traveling direction during each red light waiting period according to daily traffic flow data; acquiring the average duration of the idle traffic flows passing through the traffic intersection and the supplement time required for the number of the idle traffic flows in the next idle traveling direction to reach the idle average number again; determining the idle passing time of the idle traffic flow according to the average idle number and the average time; presetting a pedestrian crossing induction button at each traffic intersection; the pedestrian street-crossing sensing button is used for triggering the pedestrian street-crossing sensing button when a pedestrian wants to pass through the zebra crossing, the pedestrian street-crossing sensing button acquires preset waiting time, and the zebra crossing traffic signal lamp is converted into a green lamp after the preset waiting time; the pedestrian crossing induction button is connected with the zebra crossing traffic light, and after the preset waiting time is finished, the pedestrian crossing induction button sends a control signal to the zebra crossing traffic light so that the zebra crossing traffic light is changed into a green light; the method is beneficial to saving the redundant zebra crossing green light time, so that the situation that no people pass through the zebra crossing green light can be avoided, and the passing efficiency of the vehicle is improved; adjusting the peak red light time length in the peak traveling direction according to the idle passing time length and the preset green light time length after the pedestrian crossing sensing button is triggered; adjusting the peak green light time length of the peak traveling direction according to the supplement time length and the waiting time length before the zebra crossing traffic signal lamp is turned into the green light after the preset pedestrian crossing induction button is triggered; adjusting the idle green light duration of the idle traveling direction according to the idle passing duration and the waiting duration before the zebra crossing traffic signal lamp is turned to green light after the preset pedestrian crossing induction button is triggered; adjusting the idle red light duration of the idle traveling direction according to the supplement duration and the preset green light duration after the pedestrian street crossing sensing button is triggered; furthermore, after the peak time or after the real-time traffic data collected by the data collection module is analyzed, the traveling direction of the current traffic intersection is judged not to be the traveling direction of the peak traffic, and the previous traffic signal lamp adjustment strategy is recovered.
The beneficial effects of the above technical scheme are: and adjusting the traffic light time of the traffic signal light of the traffic intersection in advance according to the analysis result, and planning after congestion is compared to the situation that the traffic efficiency is improved beneficially.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. The utility model provides an wisdom traffic signal lamp networking optimization control system that coordinates which characterized in that includes:
the data acquisition module is used for acquiring traffic flow data of a plurality of traffic intersections;
the data processing module is used for processing the traffic flow data to obtain integrated information;
the instruction output module is used for outputting a corresponding signal lamp instruction according to the integrated information;
and the signal lamp control module is used for controlling the switching of the signal lamps according to the signal lamp instruction.
2. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, wherein the data acquisition module comprises a plurality of traffic camera units and a data sending unit;
each traffic camera shooting unit corresponds to a camera shooting area of one traffic intersection and has a unique number;
the traffic camera shooting unit is used for collecting vehicle data in a corresponding camera shooting area;
the data sending unit is used for integrating the vehicle data into traffic data and sending the traffic data to the data processing module; the traffic data comprises a number corresponding to the traffic camera unit, vehicle data shot by the traffic camera unit and time shot by the traffic camera unit.
3. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, wherein the data processing module comprises:
the data receiving unit is used for receiving the traffic data sent by the data acquisition module;
the data analysis and calculation unit is used for analyzing the vehicle staying number in each image pickup area in the traffic flow data when the red light is emitted, and calculating the vehicle staying time length in each image pickup area in the traffic flow data when the red light is emitted;
a data joint calculation unit for calculating the cost of the vehicle passing the intersection according to the information of the number plate of the vehicle in different camera shooting areas in the traffic flow data and the stay time of the vehicle
Average passage time length;
and the data integration and transmission unit is used for integrating the vehicle stopping number and the average passing time to obtain integration information and transmitting the integration information to the instruction output module.
4. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, wherein the instruction output module comprises:
the information receiving unit is used for receiving the integration information sent by the data processing module;
the information grouping unit is used for grouping the integrated information to obtain a plurality of groups of independent information; each group of the independent information comprises integrated information in two adjacent camera shooting areas;
the information judgment unit is used for judging whether the traffic signal lamp needs to be changed or not based on the trained and re-excited learned AI body according to the independent information to generate a judgment result;
and the result execution unit is used for generating a corresponding signal lamp instruction according to the judgment result and sending the signal lamp instruction to the signal lamp control module.
5. The intelligent traffic signal lamp networking cooperative optimization control system of claim 4, wherein the information judgment unit performs operations comprising:
acquiring independent information, and monitoring the traffic condition of adjacent camera shooting areas based on the trained and re-excited learning AI body according to the independent information;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is poor is monitored, the traffic signal lamp is judged to need to be changed, and a judgment result of reducing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the situation that the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is poor is monitored, the traffic signal lamp is judged to be required to be changed, and a judgment result of increasing the number of released vehicles at the traffic intersection corresponding to the current camera shooting area is generated;
if the traffic condition of the next camera shooting area adjacent to the current camera shooting area is good and the traffic condition of the current camera shooting area is good, the traffic signal lamp is judged not to be required to be changed, and a judgment result that the traffic signal lamp is not required to be changed is generated.
6. The intelligent traffic signal lamp networking cooperative optimization control system of claim 4, wherein if the judgment result is that no traffic signal lamp change is required, the signal lamp command generated by the result execution unit is none and is not sent to the signal lamp control module.
7. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, further comprising: an auxiliary control module;
the auxiliary control module is used for receiving and displaying the integration information sent by the data processing module, intervening the signal lamp instruction sent by the instruction output module and directly controlling the signal lamp control module;
the auxiliary control module includes:
the receiving and displaying unit is used for receiving the integration information sent by the data processing module and displaying the integration information to workers;
the instruction intervention unit is used for sending an intervention instruction to interfere the signal lamp instruction sent by the instruction output module to cause the signal lamp instruction to fail;
and the direct control unit is used for sending a new signal lamp instruction to the signal lamp control module after the signal lamp instruction fails.
8. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, further comprising: the intelligent prompt module is used for fast passing;
the quick passing intelligent reminding module executes the following operations:
during the waiting period of the red light, acquiring traffic flow images shot by a traffic camera unit, and carrying out image acquisition processing on the heads of drivers in the traffic flow images to obtain head images of a plurality of drivers;
screening the head images to obtain head images to be detected of drivers with the vehicle sequences at least in the first three positions in the traffic flow images;
inputting the head image to be detected into a human face orientation classification model which is trained in advance for processing to obtain the human face orientation of each driver in the head image to be detected;
judging whether the face orientation of each driver in the head image to be detected points to the crossing traffic direction or not to obtain a first judgment result;
acquiring the residual red light time of the traffic signal lamp, and judging whether the residual red light time is less than the preset starting time or not to obtain a second judgment result;
and when the second judgment result is that the remaining red light time is less than the preset starting time and the first judgment result is that the face orientation of any driver in the head image to be detected does not point to the crossing passing direction, sending out a rapid passing voice prompt.
9. The intelligent traffic signal lamp networking cooperative optimization control system of claim 8, wherein the fast passing intelligent reminding module further comprises: a fatigue driving monitoring module;
the fatigue driving monitoring module executes the following operations:
during the waiting period of the red light, a second traffic image pickup unit with high resolution is arranged in advance to pick up the traffic information to obtain a second traffic image;
the method comprises the steps that a traffic flow image shot by a traffic camera unit is obtained, and when the quick traffic intelligent reminding module carries out quick traffic voice reminding and a traffic signal lamp is turned into a green light, the first average passing time of a vehicle driven by a driver with face orientation classification passing through a current traffic intersection is calculated;
calculating a second average passing time length when other vehicles pass through the current traffic intersection except the vehicle driven by the driver with the face orientation classification before the current traffic signal lamp is converted into the red light;
if the difference value between the first average elapsed time length and the second average elapsed time length is greater than a preset reaction difference value;
carrying out image acquisition processing on the eyes of the driver subjected to face orientation classification in the second traffic flow image to obtain an eye image;
inputting the eye image into an eye focusing degree pre-estimation model which is trained in advance to be processed, and obtaining the focusing degree value of the eyes of the current driver in the process of waiting for the red light to pass through the green light;
judging whether the focusing degree value of eyes of a driver is lower than a preset focusing degree threshold value or not in the process of waiting for a red light to pass a green light at present;
if so, judging that the current driver has suspicion of fatigue driving, and recording the vehicle number plate of the current driver with suspicion of fatigue driving to obtain a fatigue vehicle number plate;
acquiring traffic flow images shot by all traffic camera units, and acquiring a second eye image of a driver of the fatigue vehicle shot by a second traffic camera unit corresponding to a traffic intersection if the fatigue vehicle with the same vehicle number plate as the fatigue vehicle number plate appears in the traffic flow images;
inputting the second eye image into a pre-trained eye focusing degree estimation model for processing to obtain a second focusing degree value of eyes of a current driver in the process of waiting for a red light to pass a green light;
judging whether a second focusing degree value of eyes exists at a moment when the current driver waits for the red light to pass through the green light and is lower than a preset focusing degree threshold value or not;
and if the vehicle number plate exists, sending out a fatigue driving voice prompt by combining the fatigue vehicle number plate, and sending the monitoring result to a background terminal for early warning.
10. The intelligent traffic signal lamp networking cooperative optimization control system according to claim 1, further comprising: a traffic light change advanced planning module;
the traffic light change advanced planning module executes the following operations:
acquiring daily traffic flow data of a plurality of traffic intersections through the data acquisition module;
determining the peak time of the traffic flow at each traffic intersection according to the daily traffic flow data, and determining the peak traveling direction of the peak traffic flow at the peak time of the traffic flow at each traffic intersection;
determining the idle traveling direction of idle traffic flow which does not belong to the traffic flow peak at each traffic intersection in the traffic flow peak period and the idle average number of idle traffic flow which belongs to the idle traveling direction during each red light waiting period according to the daily traffic flow data;
acquiring the average duration of the idle traffic flow passing through the traffic intersection and the supplement time required for the number of the idle traffic flow in the next idle traveling direction to reach the idle average number again;
determining the idle passing time of the idle traffic flow according to the idle average number and the average time;
presetting a pedestrian crossing induction button at each traffic intersection;
triggering a pedestrian street-crossing sensing button when a pedestrian wants to pass through the zebra crossing, wherein the pedestrian street-crossing sensing button acquires preset waiting time, and the zebra crossing traffic signal lamp is converted into a green lamp after the preset waiting time;
adjusting the peak red light time length of the peak advancing direction according to the idle passing time length and the green light time length after the preset pedestrian crossing sensing button is triggered;
adjusting the peak green time length of the peak traveling direction according to the supplement time length and the waiting time length before the zebra crossing traffic signal lamp is turned to green after the preset pedestrian crossing sensing button is triggered;
adjusting the idle green light duration of the idle traveling direction according to the idle passing duration and the waiting duration before the zebra crossing traffic signal lamp is turned to green light after the preset pedestrian crossing sensing button is triggered;
and adjusting the idle red light duration of the idle advancing direction according to the supplement duration and the green light duration after the preset pedestrian street crossing sensing button is triggered.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116758763A (en) * 2023-05-06 2023-09-15 西藏金采科技股份有限公司 Traffic data processing system and method based on Internet of vehicles
CN117542210A (en) * 2024-01-10 2024-02-09 山东华夏高科信息股份有限公司 Intelligent traffic signal lamp networking collaborative optimization control system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109584579A (en) * 2018-12-21 2019-04-05 平安科技（深圳）有限公司 Method for controlling traffic signal lights and computer equipment based on recognition of face
TW201919019A (en) * 2017-11-03 2019-05-16 英屬開曼群島商麥迪創科技股份有限公司 Traffic light change reminding system and method thereof
CN111084710A (en) * 2018-10-24 2020-05-01 上海博泰悦臻网络技术服务有限公司 Method and system for providing navigation for special user
JP2020091614A (en) * 2018-12-05 2020-06-11 住友電気工業株式会社 Information providing system, server, mobile terminal, and computer program
CN111710177A (en) * 2020-05-11 2020-09-25 华东师范大学 Intelligent traffic signal lamp networking cooperative optimization control system and control method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201919019A (en) * 2017-11-03 2019-05-16 英屬開曼群島商麥迪創科技股份有限公司 Traffic light change reminding system and method thereof
CN111084710A (en) * 2018-10-24 2020-05-01 上海博泰悦臻网络技术服务有限公司 Method and system for providing navigation for special user
JP2020091614A (en) * 2018-12-05 2020-06-11 住友電気工業株式会社 Information providing system, server, mobile terminal, and computer program
CN109584579A (en) * 2018-12-21 2019-04-05 平安科技（深圳）有限公司 Method for controlling traffic signal lights and computer equipment based on recognition of face
CN111710177A (en) * 2020-05-11 2020-09-25 华东师范大学 Intelligent traffic signal lamp networking cooperative optimization control system and control method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘宇等: "酒后驾驶员在交叉口的视觉特性研究", 《交通信息与安全》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116758763A (en) * 2023-05-06 2023-09-15 西藏金采科技股份有限公司 Traffic data processing system and method based on Internet of vehicles
CN116758763B (en) * 2023-05-06 2024-02-20 西藏金采科技股份有限公司 Traffic data processing system and method based on Internet of vehicles
CN117542210A (en) * 2024-01-10 2024-02-09 山东华夏高科信息股份有限公司 Intelligent traffic signal lamp networking collaborative optimization control system
CN117542210B (en) * 2024-01-10 2024-03-26 山东华夏高科信息股份有限公司 Intelligent traffic signal lamp networking collaborative optimization control system

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