CN113689700A - Method and device for supervising driving of construction sidewalk in mountainous area - Google Patents
Method and device for supervising driving of construction sidewalk in mountainous area Download PDFInfo
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- 230000010485 coping Effects 0.000 claims description 10
- 230000000391 smoking effect Effects 0.000 claims description 10
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
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- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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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
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Abstract
The application relates to a method and a device for supervising the driving of a sidewalk in mountainous area construction, which relate to the technical field of driving supervision and comprise the following steps: monitoring the driving behavior of a driver of a vehicle to obtain dangerous driving behavior data and establishing a behavior supervision database; monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle; monitoring a target road to obtain a road state; simulating to obtain road congestion prediction information, generating a corresponding running scheduling instruction according to the road congestion prediction information, and sending the corresponding running scheduling instruction to a corresponding vehicle; and performing road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information. This application monitors vehicle, driver and road, dispatches the vehicle according to the monitoring condition to supervise the driver action, with the realization under the prerequisite of guaranteeing safe and cost control of traveling, rationally supervise the road vehicle condition of traveling, satisfy the mountain area demand of being under construction.
Description
Technical Field
The application relates to the technical field of vehicle scheduling, in particular to a method and a device for supervising driving of a construction sidewalk in a mountainous area.
Background
The construction of large railway bridges is mostly in remote mountainous areas, and the road environment is severe. The mountain road is winding and rugged, and has transverse cliff, especially on a high-fall mountain road, the vehicle posture also seriously influences the driving safety, and safety accidents can happen carelessly, so that the loss is difficult to estimate.
Meanwhile, as the roads in the mountainous area are narrow, part of the roads can only drive in one direction, if two vehicles meet each other, one vehicle needs to back and enter the wrong vehicle platform, and the other vehicle needs to pass through in advance, so that the passing efficiency is greatly influenced, and the vehicle risk is greatly increased. At this time, how to supervise driving is particularly important for vehicle scheduling and managing driving behaviors of drivers.
Based on the technical problems, the technology for supervising the driving of the construction sidewalk in the mountainous area is provided so as to meet the driving supervision requirement of the current mountainous area road.
Disclosure of Invention
The application provides a method and a device for supervising driving of a sidewalk for mountain construction, which are used for monitoring vehicles, drivers and roads, positioning the vehicles in real time, scheduling the vehicles according to monitoring conditions and supervising the behaviors of the drivers, so that the driving conditions of the vehicles on the roads are reasonably supervised on the premise of guaranteeing the driving safety and cost control, and the requirements of mountain construction are met.
In a first aspect, the present application provides a method for supervising driving of a sidewalk in mountain construction, the method comprising the following steps:
monitoring the driving behavior of a driver of the vehicle to obtain dangerous driving behavior data, and establishing a behavior supervision database corresponding to the driver;
monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle;
monitoring the target road to obtain a road state;
according to the vehicle running state and the road state, combining the positioning information of the vehicle and the geographic information of the target road, simulating to obtain road congestion prediction information, generating a corresponding running scheduling instruction according to the road congestion prediction information, and sending the corresponding running scheduling instruction to the corresponding vehicle;
and carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
Further, the method comprises the following steps:
monitoring the driving behavior of a driver of the vehicle on a preset key road section to obtain driving coping behavior data;
managing the driving response behavior data and the key road sections, and storing the driving response behavior data and the key road sections into the behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
Specifically, the vehicle running state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
In particular, the dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
Specifically, the road state includes road congestion, falling rocks or landslide.
Further, the method comprises the following steps:
and forwarding to the corresponding vehicle according to the received voice scheduling instruction.
In a second aspect, the present application provides a mountain area construction access road driving supervision device, the device includes:
the driver monitoring module is used for monitoring the driving behavior of a driver of the vehicle, acquiring dangerous driving behavior data and establishing a behavior supervision database corresponding to the driver;
the vehicle monitoring module is used for monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle;
the road monitoring module is used for monitoring the target road to obtain a road state;
the driving scheduling module is used for simulating and obtaining road congestion prediction information according to the vehicle driving state and the road state and by combining the positioning information of the vehicle and the geographic information of the target road, generating a corresponding driving scheduling instruction according to the road congestion prediction information and sending the corresponding driving scheduling instruction to the corresponding vehicle;
and the running supervision module is used for carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
Further, the driver monitoring module is further configured to monitor driving behaviors of a driver of the vehicle on a preset key road section to obtain driving response behavior data;
the driving supervision module is also used for managing the driving response behavior data and the key road sections and storing the driving response behavior data and the key road sections into the behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
Specifically, the vehicle running state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
In particular, the dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
The beneficial effect that technical scheme that this application provided brought includes:
this application monitors vehicle, driver and road, fixes a position the vehicle in real time, dispatches the vehicle according to the monitoring condition to supervise the driver action, with the realization under the prerequisite of guaranteeing safety and cost control of traveling, rationally supervise the road vehicle condition of traveling, satisfy the mountain area construction demand.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a method for supervising driving of a sidewalk in a mountainous area construction;
FIG. 2 is a schematic frame diagram of a method for monitoring driving of a sidewalk in a mountain construction according to an embodiment of the present disclosure;
fig. 3 is a structural block diagram of a traffic monitoring device for a sidewalk in a mountain construction provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a method and a device for supervising driving of a sidewalk for mountain construction, vehicles, drivers and roads are monitored, the vehicles are positioned in real time, the vehicles are dispatched according to monitoring conditions, the behaviors of the drivers are supervised, the driving conditions of the vehicles are reasonably supervised on the premise of guaranteeing driving safety and cost control, and requirements of mountain construction are met.
In order to achieve the technical effects, the general idea of the application is as follows:
a method for supervising the driving of a sidewalk in mountain construction comprises the following steps:
s1, monitoring the driving behavior of a driver of the vehicle to obtain dangerous driving behavior data, and establishing a behavior supervision database corresponding to the driver;
s2, monitoring the driving state of the vehicle on the target road to obtain the driving state of the vehicle;
s3, monitoring the target road to obtain a road state;
s4, according to the running state and the road state of the vehicle, combining the positioning information of the vehicle and the geographic information of the target road, simulating to obtain road congestion prediction information, generating a corresponding running scheduling instruction according to the road congestion prediction information, and sending the corresponding running scheduling instruction to the corresponding vehicle;
and S5, performing road safety early warning based on the dangerous driving behavior data, the vehicle driving state and the road congestion prediction information.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In a first aspect, referring to fig. 1 to 2, an embodiment of the present application provides a method for supervising driving of a sidewalk in a mountain construction, where the method includes the following steps:
s1, monitoring the driving behavior of a driver of the vehicle to obtain dangerous driving behavior data, and establishing a behavior supervision database corresponding to the driver;
s2, monitoring the driving state of the vehicle on the target road to obtain the driving state of the vehicle;
s3, monitoring the target road to obtain a road state;
s4, according to the running state and the road state of the vehicle, combining the positioning information of the vehicle and the geographic information of the target road, simulating to obtain road congestion prediction information, generating a corresponding running scheduling instruction according to the road congestion prediction information, and sending the corresponding running scheduling instruction to the corresponding vehicle;
and S5, performing road safety early warning based on the dangerous driving behavior data, the vehicle driving state and the road congestion prediction information.
In the embodiment of the application, the vehicle, the driver and the road are monitored, the vehicle is positioned in real time, the vehicle is dispatched according to the monitoring condition, the behavior of the driver is supervised, the driving condition of the road vehicle is reasonably supervised on the premise of ensuring the driving safety and controlling the cost, and the construction requirement of the mountainous area is met.
In the embodiment of the application, the method comprises the following steps:
the method comprises the following steps of firstly, detecting the position and the speed of a vehicle in real time based on a Beidou navigation system.
Secondly, arranging a camera in the vehicle, wherein the camera is inclined upwards by 15 degrees at 30-45 degrees on the right side of the driving position, and the camera can capture facial features of a driver, so that the behavior state of the driver is obtained;
in addition, the camera sets a starting condition: when the vehicle speed is more than 20km/h, automatically starting intelligent monitoring, and when the vehicle speed is less than 20km/h, determining to determine the safe driving speed without judgment;
the method comprises the following steps of detecting abnormal driving behaviors of a driver in real time by utilizing edge computing power and an artificial intelligence algorithm: eye closure, fatigue driving (yawning), inattention (long-time left-right anticipation), smoking and calling, dangerous driving behavior data are generated according to abnormal driving behaviors of a driver, the dangerous driving behavior data are recorded, and a behavior supervision database corresponding to the driver is established.
Thirdly, installing high-speed snapshot equipment at the entrance and exit of the target road, and counting the vehicles in the road section;
and deploying a camera in a preset key area of the target road, and monitoring falling rocks, collapse, vehicle overload and congestion detection through a deep learning artificial intelligence algorithm.
Fourthly, generating a corresponding driving scheduling instruction according to the driving state of the vehicle, the behavior information of the driver and the road state and by combining the positioning information of the vehicle and the geographic information of the target road, and scheduling and commanding the vehicle in a visual interface and voice mode;
and fifthly, carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
It should be noted that, the area is preset by the electronic fence, when the vehicle travels to the preset monitoring section,
entering a monitoring road section, actively reminding the driver to slow down by voice, and if the speed is more than 30KM/H, reminding the driver to slow down by equipment voice;
according to the positioning information of the vehicle, the vehicle is detected to enter a sharp bend area, and the driver is actively reminded of slowing down in advance by voice to avoid sharp braking and sharp bend;
when the vehicle is detected to have rapid acceleration, rapid braking and rapid turning, the voice reminds the driver to decelerate in advance, accelerate slowly and turn;
when the driver is detected to have abnormal driving behaviors, namely, behaviors of closing eyes, fatigue driving (yawning), inattention (long-time left-hand desire), smoking, calling and the like, the driver is reminded through voice.
Concretely, explain road safety early warning, insert monitoring early warning system, according to the actual conditions at scene, calculate the early warning, wherein, divide the early warning grade into the tertiary, the condition as follows:
primary early warning, wherein the real-time traffic risk evaluation is scored for 1-5 points to remind a driver to slow down;
secondary early warning, wherein the real-time traffic risk evaluation is scored for 6-9 points, the driver is required to pay attention to the real-time traffic risk evaluation, and the information is pushed to a manager; if the road condition is caused by natural factors, corresponding control measures are adopted, and the traffic continues after the road condition is adjusted; if the artificial factors are included to trigger the secondary alarm, oral education is carried out on the driver;
three-stage early warning, wherein the real-time traffic risk evaluation score is larger than 10, if the real-time traffic risk evaluation score is triggered by external factors, a driver should stop traffic immediately, and relevant responsible persons perform road maintenance; if the driver is triggered due to the reason of the driver, the post-training education is forced to be performed again;
the real-time peer risk consists of natural factor risk and human factor risk; the risk of the natural factors is a fixed value and is reduced to 0 until the exception is processed; the human factor risks are accumulated in the whole monitoring area process in the same row, and are reduced to 0 after being out of the area;
for example:
landslide exists in the road section, the risk is natural factor risk, the risk value is 10, and the platform directly carries out three-stage early warning; the management personnel immediately performs traffic control, and the same line is recovered after the risk is relieved;
when a road section is jammed, the risk is a natural factor risk, the risk value is 4, the platform carries out primary early warning, and voice broadcasting is carried out on vehicles in the same driving; in the process, if the driver smokes and makes a call, the passing risk of the driver is increased to 6, and secondary early warning is immediately started; the message is pushed to a manager, and after the manager conducts criticizing education on the driver, secondary alarm is relieved;
the road section has no risk of natural factors, a driver triggers a primary early warning in the passing process, and the driver automatically contacts the monitoring area after the driver passes through the monitoring area; if the secondary early warning is triggered, the secondary warning is relieved after the manager carries out criticizing education on the driver; if the third-level early warning is triggered, the driver can forcibly quit the post and be subjected to educational training, and the driver can check and go on the post again;
all the alarms triggered by the driver are connected to a personal assessment and evaluation system (deduction system) of the driver, and if the accumulated integral is lower than 70 minutes, the driver is forced to quit the post, is subjected to educational training, and is assessed to go on the post again.
Referring to fig. 2 of the drawings in the specification, which is a schematic frame diagram of the method according to the embodiment of the application, table 1 below shows the safety scoring standards of drivers and roads, some of which are natural disasters and some of which are human factors, when a natural disaster occurs, corresponding emergency measures should be taken in time, and personnel factors should be stopped as much as possible.
TABLE 1 driver and road safety Scoring Standard
According to the risk scores in the table 1, driving risk assessment is carried out in real time, and the risk grades are divided into three grades:
primary early warning, and scoring 1-5 points for real-time traffic risk evaluation;
secondary early warning, and scoring 6-9 points for real-time traffic risk evaluation;
three-stage early warning, wherein the real-time traffic risk evaluation score is more than 10;
the risk generated by the driver (human in the table 1) factor is accessed into the driver assessment system to carry out the reduction and division (full division is 100), the personal safety score of the driver is lower than 80 minutes, the manager carries out oral education on the driver, the safety score is obtained by forced exit learning when the personal safety score is lower than 60 minutes, and the entrance is allowed when the safety score is increased to 100 minutes. The driver can obtain points by receiving safety training and safety video education.
Further, the method for supervising the driving of the sidewalk in the mountainous area construction further comprises the following steps:
monitoring the driving behavior of a driver of a vehicle on a preset key road section to obtain driving coping behavior data;
managing the driving response behavior data and the key road sections, and storing the driving response behavior data and the key road sections into a behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
Specifically, the vehicle driving state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
In particular, dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
Specifically, the road state includes road congestion, falling rocks, or landslide.
Further, the method comprises the following steps:
and forwarding to the corresponding vehicle according to the received voice scheduling instruction.
In a second aspect, referring to fig. 3, an embodiment of the present application provides a driving supervision apparatus for a sidewalk in a mountainous area construction, which is based on the driving supervision method for a sidewalk in a mountainous area construction mentioned in the first aspect, and the apparatus includes:
the driver monitoring module is used for monitoring the driving behavior of a driver of the vehicle to obtain dangerous driving behavior data;
the vehicle monitoring module is used for monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle;
the road monitoring module is used for monitoring a target road to obtain a road state;
the driving scheduling module is used for simulating and obtaining road congestion prediction information according to the driving state of the vehicle and the road state and by combining the positioning information of the vehicle and the geographic information of the target road, generating a corresponding driving scheduling instruction according to the road congestion prediction information and sending the corresponding driving scheduling instruction to the corresponding vehicle;
and the driving supervision module is used for recording dangerous driving behavior data and establishing a behavior supervision database corresponding to the driver.
In the embodiment of the application, the vehicle, the driver and the road are monitored, the vehicle is positioned in real time, the vehicle is dispatched according to the monitoring condition, the behavior of the driver is supervised, the driving condition of the road vehicle is reasonably supervised on the premise of ensuring the driving safety and controlling the cost, and the construction requirement of the mountainous area is met.
In the embodiment of the present application, the apparatus, when implemented specifically, includes the following processes:
the method comprises the following steps of firstly, detecting the position and the speed of a vehicle in real time based on a Beidou navigation system.
Secondly, arranging a camera in the vehicle, wherein the camera is inclined upwards by 15 degrees at 30-45 degrees on the right side of the driving position, and the camera can capture facial features of a driver, so that the behavior state of the driver is obtained;
in addition, the camera sets a starting condition: when the vehicle speed is more than 20km/h, automatically starting intelligent monitoring, and when the vehicle speed is less than 20km/h, determining to determine the safe driving speed without judgment;
the method comprises the following steps of detecting abnormal driving behaviors of a driver in real time by utilizing edge computing power and an artificial intelligence algorithm: eye closure, fatigue driving (yawning), inattention (long-time left-right anticipation), smoking and calling, dangerous driving behavior data are generated according to abnormal driving behaviors of a driver, the dangerous driving behavior data are recorded, and a behavior supervision database corresponding to the driver is established.
Thirdly, installing high-speed snapshot equipment at the entrance and exit of the target road, and counting the vehicles in the road section;
and deploying a camera in a preset key area of the target road, and monitoring falling rocks, collapse, vehicle overload and congestion detection through a deep learning artificial intelligence algorithm.
Fourthly, generating a corresponding driving scheduling instruction according to the driving state of the vehicle, the behavior information of the driver and the road state and by combining the positioning information of the vehicle and the geographic information of the target road, and scheduling and commanding the vehicle in a visual interface and voice mode;
and fifthly, carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
It should be noted that, the area is preset by the electronic fence, when the vehicle travels to the preset monitoring section,
entering a monitoring road section, actively reminding the driver to slow down by voice, and if the speed is more than 30KM/H, reminding the driver to slow down by equipment voice;
according to the positioning information of the vehicle, the vehicle is detected to enter a sharp bend area, and the driver is actively reminded of slowing down in advance by voice to avoid sharp braking and sharp bend;
when the vehicle is detected to have rapid acceleration, rapid braking and rapid turning, the voice reminds the driver to decelerate in advance, accelerate slowly and turn;
when the driver is detected to have abnormal driving behaviors, namely, behaviors of closing eyes, fatigue driving (yawning), inattention (long-time left-hand desire), smoking, calling and the like, the driver is reminded through voice.
Concretely, explain road safety early warning, insert monitoring early warning system, according to the actual conditions at scene, calculate the early warning, wherein, divide the early warning grade into the tertiary, the condition as follows:
primary early warning, wherein the real-time traffic risk evaluation is scored for 1-5 points to remind a driver to slow down;
secondary early warning, wherein the real-time traffic risk evaluation is scored for 6-9 points, the driver is required to pay attention to the real-time traffic risk evaluation, and the information is pushed to a manager; if the road condition is caused by natural factors, corresponding control measures are adopted, and the traffic continues after the road condition is adjusted; if the artificial factors are included to trigger the secondary alarm, oral education is carried out on the driver;
three-stage early warning, wherein the real-time traffic risk evaluation score is larger than 10, if the real-time traffic risk evaluation score is triggered by external factors, a driver should stop traffic immediately, and relevant responsible persons perform road maintenance; if the driver is triggered due to the reason of the driver, the post-training education is forced to be performed again;
the real-time peer risk consists of natural factor risk and human factor risk; the risk of the natural factors is a fixed value and is reduced to 0 until the exception is processed; the human factor risks are accumulated in the whole monitoring area process in the same row, and are reduced to 0 after being out of the area;
for example:
landslide exists in the road section, the risk is natural factor risk, the risk value is 10, and the platform directly carries out three-stage early warning; the management personnel immediately performs traffic control, and the same line is recovered after the risk is relieved;
when a road section is jammed, the risk is a natural factor risk, the risk value is 4, the platform carries out primary early warning, and voice broadcasting is carried out on vehicles in the same driving; in the process, if the driver smokes and makes a call, the passing risk of the driver is increased to 6, and secondary early warning is immediately started; the message is pushed to a manager, and after the manager conducts criticizing education on the driver, secondary alarm is relieved;
the road section has no risk of natural factors, a driver triggers a primary early warning in the passing process, and the driver automatically contacts the monitoring area after the driver passes through the monitoring area; if the secondary early warning is triggered, the secondary warning is relieved after the manager carries out criticizing education on the driver; if the third-level early warning is triggered, the driver can forcibly quit the post and be subjected to educational training, and the driver can check and go on the post again;
all the alarms triggered by the driver are connected to a personal assessment and evaluation system (deduction system) of the driver, and if the accumulated integral is lower than 70 minutes, the driver is forced to quit the post, is subjected to educational training, and is assessed to go on the post again.
Further, the driver monitoring module is further configured to monitor driving behaviors of a driver of the vehicle on a preset key road section to obtain driving response behavior data;
the driving supervision module is also used for managing the driving response behavior data and the key road sections and storing the driving response behavior data and the key road sections into a behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
Specifically, the vehicle driving state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
In particular, dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
Specifically, the road state includes road congestion, falling rocks, or landslide.
It is noted that, in the present application, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present application and are presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for supervising the driving of a sidewalk in mountain construction is characterized by comprising the following steps:
monitoring the driving behavior of a driver of the vehicle to obtain dangerous driving behavior data, and establishing a behavior supervision database corresponding to the driver;
monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle;
monitoring the target road to obtain a road state;
according to the vehicle running state and the road state, combining the positioning information of the vehicle and the geographic information of the target road, simulating to obtain road congestion prediction information, generating a corresponding running scheduling instruction according to the road congestion prediction information, and sending the corresponding running scheduling instruction to the corresponding vehicle;
and carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
2. The method for supervising driving of a sidewalk in mountain construction according to claim 1, wherein the method further comprises the steps of:
monitoring the driving behavior of a driver of the vehicle on a preset key road section to obtain driving coping behavior data;
managing the driving response behavior data and the key road sections, and storing the driving response behavior data and the key road sections into the behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
3. The method for supervising the driving of the sidewalk in the mountainous area construction according to claim 1, wherein:
the vehicle running state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
4. The method for supervising the driving of the sidewalk in the mountainous area construction according to claim 1, wherein:
the dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
5. The method for supervising the driving of the sidewalk in the mountainous area construction according to claim 1, wherein:
the road state comprises road congestion, falling rocks or landslide.
6. The method for supervising driving of a sidewalk in mountain construction according to claim 1, wherein the method further comprises the steps of:
and forwarding to the corresponding vehicle according to the received voice scheduling instruction.
7. The utility model provides a mountain area construction pavement driving supervision device which characterized in that, the device includes:
the driver monitoring module is used for monitoring the driving behavior of a driver of the vehicle, acquiring dangerous driving behavior data and establishing a behavior supervision database corresponding to the driver;
the vehicle monitoring module is used for monitoring the running state of the vehicle on the target road to obtain the running state of the vehicle;
the road monitoring module is used for monitoring the target road to obtain a road state;
the driving scheduling module is used for simulating and obtaining road congestion prediction information according to the vehicle driving state and the road state and by combining the positioning information of the vehicle and the geographic information of the target road, generating a corresponding driving scheduling instruction according to the road congestion prediction information and sending the corresponding driving scheduling instruction to the corresponding vehicle;
and the running supervision module is used for carrying out road safety early warning based on the dangerous driving behavior data, the vehicle running state and the road congestion prediction information.
8. The mountain area construction pavement driving supervision device of claim 7, characterized in that:
the driver monitoring module is also used for monitoring the driving behavior of a driver of the vehicle on a preset key road section to obtain driving coping behavior data;
the driving supervision module is also used for managing the driving response behavior data and the key road sections and storing the driving response behavior data and the key road sections into the behavior supervision database; wherein the content of the first and second substances,
and the driving coping behavior data is used for recording whether the driver performs continuous light braking on the key road section.
9. The mountain area construction pavement driving supervision device of claim 7, characterized in that:
the vehicle running state comprises a vehicle overspeed state, a vehicle sudden braking state, a vehicle sudden acceleration state and a vehicle sudden turning state.
10. The mountain area construction pavement driving supervision device of claim 7, characterized in that:
the dangerous driving behavior data includes eye closure, fatigue driving, inattention, smoking, and making a call.
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