CN117392844A - Road traffic safety monitoring method, equipment and medium - Google Patents
Road traffic safety monitoring method, equipment and medium Download PDFInfo
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G—TRAFFIC CONTROL SYSTEMS
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- 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
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
- G08G1/054—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed photographing overspeeding vehicles
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Abstract
The application discloses a road traffic safety monitoring method, equipment and medium, wherein the method comprises the following steps: acquiring a road image of a road area in a preset period; outputting vehicle information of a road area by inputting a road image into a pre-trained vehicle recognition neural network model; judging whether the vehicle flow exceeds a preset flow threshold; if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp corresponding to the vehicle flow to shorten the conversion period; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period; determining a first control time for shortening a conversion period of the temporary signal lamp according to a difference value between the vehicle flow and a preset flow threshold; and in the first control time, adjusting the signal lamp conversion period of the road area to be a temporary signal lamp conversion period. And the road traffic safety monitoring efficiency is improved.
Description
Technical Field
The application relates to the technical field of big data analysis, in particular to a road traffic safety monitoring method, equipment and medium.
Background
As traffic becomes more and more convenient, traffic safety monitoring platforms have grown to enable monitoring, management and emergency response to traffic safety.
The traffic condition of the traffic intersection can be monitored in real time through the traffic monitoring system. When traffic jams or accidents are found, traffic personnel often need to do down-line wisdom in the road area to alleviate the road situation, but this approach often consumes a lot of manpower and the road situation is not alleviated timely, resulting in inefficient road traffic safety monitoring.
Disclosure of Invention
The embodiment of the application provides a road traffic safety monitoring method, equipment and medium, which are used for solving the problem of low road traffic safety monitoring efficiency.
The embodiment of the application adopts the following technical scheme:
in one aspect, an embodiment of the present application provides a road traffic safety monitoring method, including: acquiring a road image of a road area in a preset period; outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate; judging whether the vehicle flow exceeds a preset flow threshold; if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period; determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold; and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
In one example, the determining the first control time for shortening the conversion period by the temporary signal lamp according to the difference between the vehicle flow and the preset flow threshold specifically includes: acquiring historical vehicle information of the road area in a preset historical time length; generating a vehicle flow trend graph of the road area every day according to the historical vehicle information; predicting the current day vehicle flow of the road area after the current time according to the vehicle flow trend graph; determining a vehicle flow prediction time when the predicted vehicle flow is lower than the preset flow threshold value from the current time; and determining the difference value between the predicted time of the vehicle flow and the current time as a first control time for shortening a conversion period of the temporary signal lamp.
In one example, the method further comprises: determining an average vehicle speed for the road area based on the vehicle speed for each vehicle in the road area; if the vehicle flow does not exceed the preset flow threshold, judging whether the average speed of the vehicle is smaller than a preset speed threshold; if yes, matching the average speed of the vehicle in the signal lamp conversion period mapping table to obtain a temporary signal lamp increase conversion period corresponding to the average speed of the vehicle; determining a second control time of the temporary signal lamp increase conversion period according to a difference value between the average speed of the vehicle and the preset speed threshold; and in the second control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp increase conversion period.
In one example, the determining the second control time of the temporary signal light increasing conversion period according to the difference between the average speed of the vehicle and the preset speed threshold specifically includes: generating a vehicle average speed trend graph of the road area every day according to the historical vehicle information of the road area within a preset historical time period; predicting the average speed of the vehicle on the current day after the current time of the road area according to the average speed trend graph of the vehicle; starting from the current time, determining a vehicle average speed prediction time when the predicted vehicle average speed is higher than the preset speed threshold value; and determining a difference between the vehicle average speed predicted time and the current time as a second control time of the winker increase conversion period.
In one example, the method further comprises: determining a forbidden stop area of the road area; selecting a plurality of parked vehicles with a vehicle speed of 0 from the vehicle information; judging whether the parked vehicles in the forbidden area exist or not according to the vehicle position of each parked vehicle; if yes, determining the vehicle in the forbidden parking area as an illegal parking vehicle; and sending punishment notification information to a user terminal of the illegal parking vehicle according to the vehicle license plate information of the illegal parking vehicle.
In one example, the method further comprises: determining a turning-around forbidden area of the road area; generating respective corresponding vehicle trajectories for each vehicle according to a plurality of vehicle positions of each vehicle; judging whether vehicles in the area of forbidden turning around exist or not according to the respective corresponding vehicle track of each vehicle; if yes, determining the vehicle in the area where the turning is forbidden as an illegal turning vehicle; and sending punishment notification information to a user terminal of the illegal turning-around vehicle according to the vehicle license plate information of the illegal turning-around vehicle.
In one example, after the acquiring the road image of the road area, the method further includes: outputting road surface condition information of the road area by inputting the road image into a pre-trained road surface condition recognition neural network model; the pavement condition information comprises ponding, icing and hollowness; generating a road surface running dangerous grade of the road area according to the road surface state information; and when the road surface running danger level is higher than a preset level, sending road surface running danger notification information to road early warning display equipment.
In one example, the acquiring the road image of the road area specifically includes: receiving encrypted road image information of a road area; the encrypted road image information is obtained by asymmetric encryption through an image shooting device; decrypting the encrypted road image information according to a private key to obtain an initial road image of the road area; carrying out integrity verification on the initial road image, and if the verification is successful, determining the initial road image as the road image of the road area; and if the verification fails, sending the re-uploading road image notification information to the image equipment.
In another aspect, an embodiment of the present application provides a road traffic safety monitoring device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring a road image of a road area in a preset period; outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate; judging whether the vehicle flow exceeds a preset flow threshold; if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period; determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold; and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
In another aspect, embodiments of the present application provide a road traffic safety monitoring non-volatile computer storage medium storing computer-executable instructions configured to: acquiring a road image of a road area in a preset period; outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate; judging whether the vehicle flow exceeds a preset flow threshold; if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period; determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold; and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
the vehicle information can be acquired by using the vehicle identification neural network model, and the temporary signal lamp conversion period is searched from the signal lamp conversion period mapping table under the condition that the vehicle flow based on the road area is relatively high, so that the time of the signal lamp conversion period is automatically adjusted under the condition that the vehicle flow is high, the traffic flow at the current time can be optimized, and the traffic efficiency of the vehicle is increased. And the time difference between the vehicle flow and the preset flow threshold value is controlled to control the temporary signal lamp to shorten the execution time of the transformation period, so that the traffic in the short-term future time length of the vehicle can be optimized by more accurately considering the condition of the vehicle flow in the short-term future time length of the road area, and the traffic efficiency of the road area is further improved. Thereby improving the road traffic safety monitoring efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a road traffic safety monitoring method according to an embodiment of the present application;
Fig. 2 is a schematic structural diagram of a road traffic safety monitoring device according to an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a road traffic safety monitoring method according to an embodiment of the present application. Some of the input parameters or intermediate results in the flow allow for manual intervention adjustments to help improve accuracy.
The implementation of the analysis method according to the embodiment of the present application may be a terminal device or a server, which is not particularly limited in this application. For ease of understanding and description, the following embodiments are described in detail with reference to a server.
It should be noted that the server may be a single device, or may be a system formed by a plurality of devices, that is, a distributed server, which is not specifically limited in this application.
The flow in fig. 1 may include the steps of:
s101: and acquiring a road image of the road area in a preset period.
In some embodiments of the present application, the safety monitoring platform needs to ensure accuracy and real-time of vehicle information, and thus needs to solve the stability and efficiency problems of road image acquisition and transmission. This may involve reliability of the network connection, selection of the data transmission protocol, and handling of data loss or transmission errors. In addition, in the road image acquisition and transmission process, an encrypted data verification mechanism is adopted, so that the integrity and the accuracy of the road image are ensured.
Based on this, first, the encrypted road image information of the road area is received. The encrypted road image information is obtained through asymmetric encryption by the image shooting equipment.
The server generates a pair of public and private key pairs in advance, then sends the public key to the image capturing device, and the road image captured by the image capturing device is subjected to public key encryption.
And then, decrypting the encrypted road image information according to the private key to obtain an initial road image of the road area.
And then, carrying out integrity verification on the initial road image, and if the verification is successful, determining the initial road image as a road image of the road area.
In the process of integrity verification, the initial road image is compared with a preset integrity condition. Such as whether the initial road image is clear or empty.
And if the verification fails, sending the re-uploading road image notification information to the image equipment.
It should be noted that, a reasonable retry number and interval time need to be set to avoid excessively large delay to data transmission.
In some embodiments of the present application, the impact of data loss or errors may be reduced by a fault tolerant mechanism during road image acquisition and transmission. For example, backup schemes of backup data sources, backup tunnels, etc. may be used to ensure that road images can still be acquired and transmitted in the event of a failure of the primary data source or tunnel.
During road image acquisition and transmission, an error logging mechanism may be added to record the occurrence of data loss or error conditions. These logs may be used for subsequent data recovery or problem analysis.
In the road image acquisition and transmission process, data can be backed up regularly, and corresponding backup strategies are formulated. When data loss or error occurs, the data can be recovered through backup so as to reduce the influence caused by the data loss or error.
S102: and outputting the vehicle information of the road area by inputting the road image into a pre-trained vehicle identification neural network model.
The vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate.
In some embodiments of the present application, it is desirable to pre-train a vehicle identification neural network model.
Specifically, a sample road image of a sample road area is obtained, the sample road image is used as an input sample, vehicle information is used as a sample label, and the neural network model is subjected to supervised training until the neural network model converges, so that a vehicle identification neural network model is obtained.
S103: and judging whether the vehicle flow exceeds a preset flow threshold.
S104: if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow.
The time signal lamp shortens the conversion period to be shorter as the vehicle flow is larger, so that the passing efficiency of the vehicle is improved.
It should be noted that each traffic flow will get access right in one signal lamp conversion period. Taking the crossroad as an example, the traffic flow in four directions is divided into two signal stages for release, stage 1 for release in east and west, and stage 2 for release in south and north. Assuming that the sum of the time lengths of phase 1 and phase 2 is 100 seconds, the period of this signal is 100 seconds. During this 100 seconds, each traffic flow gets access rights. A relatively short signal transition period reduces latency. That is, when a green light is obtained, the release time is short, and when a red light is obtained, the waiting time is also short.
Wherein, because the road conditions of different roads are different, a signal lamp conversion period mapping table is generated for each road area.
S105: and determining a first control time for shortening the conversion period of the temporary signal lamp according to the difference value between the vehicle flow and the preset flow threshold.
Wherein the larger the difference value, the longer the first control time.
In some embodiments of the present application, since the vehicle information of the road area may be regular every day, for example, at the time of going to work and the time of going from work every day, the number of vehicles of the road area is large. Thus, short-term vehicle information of the road area after the current time can be predicted by the historical vehicle information.
Based on this, first, history vehicle information of the road area within a preset history period is acquired. Then, a map of the vehicle flow patterns of the road area on a daily basis is generated from the historical vehicle information. Then, the current day vehicle flow of the road area after the current time is predicted from the vehicle flow trend map. Then, starting from the current time, a vehicle flow prediction time is determined at which the predicted vehicle flow is below a preset flow threshold.
That is, the current time vehicle flow is relatively high, the vehicle flow between the current time and the vehicle flow prediction time is still relatively high, and the vehicle flow decreases from a short time after the vehicle flow prediction time. However, some portion of the time after the predicted time of vehicle flow may still be higher in vehicle flow, but later from the current time.
Finally, the difference between the predicted time and the current time of the vehicle flow is determined as a first control time for shortening the conversion period by the winker.
S106: and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
In some embodiments of the present application, when the vehicle flow does not exceed the preset flow threshold, it is indicated that the vehicle flow is not large, and the vehicle flow is generally small due to the small vehicle speed. Therefore, at this time, the average speed of the vehicle is continuously combined, and whether or not congestion occurs in the road area is monitored. In the process of congestion, the signal lamp conversion period is prolonged, namely, the traffic speed of vehicles is slowed down, and the congestion condition is relieved. Longer signal periods reduce the number of stops, but increase the waiting time. That is, when a green light is obtained, the release time is long, and when a red light is obtained, the waiting time is also long.
Based on this, an average vehicle speed of the road area is determined from the vehicle speed of each vehicle in the road area.
And judging whether the average speed of the vehicle is smaller than a preset speed threshold value.
If yes, the average speed of the vehicle is matched in the signal lamp conversion period mapping table, and a temporary signal lamp growth conversion period corresponding to the average speed of the vehicle is obtained.
And determining a second control time of the increase conversion period of the temporary signal lamp according to the difference value between the average speed of the vehicle and the preset speed threshold value.
And in the second control time, adjusting the signal lamp conversion period of the road area to be a temporary signal lamp increase conversion period.
The process of determining the second control time of the temporary signal lamp increasing conversion period is as follows:
first, a map of average speed of the vehicle of the road area on a daily basis is generated based on historical vehicle information of the road area within a preset historical time period. Then, the average speed of the vehicle on the day after the current time of the road area is predicted from the average speed trend map of the vehicle. Then, from the current time, a vehicle average speed prediction time at which the predicted vehicle average speed is higher than a preset speed threshold is determined.
That is, the vehicle average speed at the present time is relatively low, the vehicle average speed between the present time and the vehicle average speed prediction time is still relatively low, and the vehicle average speed rises from a short-term time after the vehicle average speed prediction time. However, some portion of the time after the predicted time of the average speed of the vehicle may still be lower in average speed of the vehicle, but later from the current time.
Finally, a difference between the vehicle average speed predicted time and the current time is determined as a second control time of the winker increase conversion period.
In some embodiments of the present application, real-time monitoring of road traffic conditions and offending vehicle identification are implemented.
In one aspect, first, a keep-out area of a road area is determined. Then, in the vehicle information, a plurality of parked vehicles whose vehicle speeds are 0 are selected. Then, it is determined whether there is a parked vehicle in the no-parking area based on the vehicle position of each parked vehicle.
If yes, the vehicle in the forbidden area is determined to be the illegal parking vehicle. And sending punishment notification information to the user terminal of the illegal parking vehicle according to the vehicle license plate information of the illegal parking vehicle.
On the other hand, first, a u-turn prohibition area of the road area is determined. Then, a respective vehicle track is generated for each vehicle based on the plurality of vehicle positions for each vehicle. Then, whether the vehicle is in the area of preventing the turning around is judged according to the corresponding vehicle track of each vehicle.
If yes, the vehicle in the area where the turning is forbidden is determined to be the illegal turning vehicle. And finally, according to the vehicle license plate information of the illegal turning-around vehicle, sending punishment notification information to the user terminal of the illegal turning-around vehicle.
In some embodiments of the present application, road conditions are monitored in real time, and road condition early warning services are provided to reduce the occurrence rate of traffic accidents. Different risks can be evaluated according to the road surface condition, and grading early warning is carried out according to the risk degree. The early warning signals of different levels can remind the driver to take different countermeasures, for example, the driver is reminded to slow down under the low risk condition, and the driver can be reminded to park or select other routes under the high risk condition.
Based on this, the road condition information of the road area is output by inputting the road image into the pre-trained road surface condition recognition neural network model. The road surface condition information comprises ponding, icing and potholes.
And generating the road surface running danger level of the road area according to the road surface state information.
And when the road surface running danger level is higher than the preset level, sending road surface running danger notification information to road early warning display equipment.
The road surface state recognition neural network model is subjected to supervised training until the road surface state recognition neural network model converges, so that the road surface state recognition neural network model is obtained.
In some embodiments of the present application, mobile applications and websites are provided, so that users can view traffic conditions, road information, traffic accidents, etc. at any time, and real-time traffic navigation and early warning services can be provided.
In addition, through the traffic safety monitoring system, the traffic condition of the traffic crossing and the working state of the signal lamp can be monitored in real time. When traffic jams or accidents are found, the manager can manually adjust the setting of the signal lamp or start an emergency plan to cope with the emergency.
Safety monitoring platforms are typically required to process large amounts of vehicle data, including real-time data and historical data. Thus, platforms need to have high performance data storage and processing capabilities to support rapid querying, analysis, and decision making of data.
The safety monitoring platform relates to sensitive information of vehicles and drivers, such as positions, running tracks and the like. The platform needs to take security measures to protect confidentiality, integrity and availability of data, and to prevent data leakage and unauthorized access.
In some embodiments of the present application, vehicle information may also be obtained by sensors, and the system architecture is as follows:
video monitoring system: the cameras comprise network cameras, high-definition cameras, panoramic cameras and the like, are used for monitoring traffic road sections in real time, acquiring road images, and the video analysis technology comprises face recognition, license plate recognition, behavior analysis and the like, and is used for detecting illegal behaviors and accident situations.
Traffic vehicle sensor: the device is arranged on the roadside lamp and used for detecting information such as flow, speed and distance of vehicles.
An intelligent traffic light control system: the traffic signal lamps are connected through the network, so that intelligent scheduling can be realized, light control is performed according to real-time traffic conditions, and traffic flow is optimized.
Data analysis and big data technology: and analyzing the big data of the collected data, identifying traffic modes, predicting traffic jams, and optimizing traffic planning and scheduling. And mass data is processed by utilizing machine learning and artificial intelligence technology, useful information is extracted, and traffic safety management is improved.
Cloud computing technology: and storing the acquired road image into cloud data, and realizing centralized management, backup, sharing and remote access of the data.
Geographic Information System (GIS): the GIS technology combines map data and vehicle position information, and can realize space analysis and visual display of treatment overload management, including the functions of positioning and route planning of illegal vehicles and the like.
Mobile application: and providing a mobile phone application program, so that the public can acquire traffic information in real time, report accidents or dangerous situations and receive traffic safety prompts.
Furthermore, device compatibility needs to be considered: the safety monitoring platform needs to communicate and exchange data with the vehicle-mounted terminal equipment, but different vehicles and equipment can adopt different communication protocols and data formats. Therefore, the platform needs to solve the problem of device compatibility, and ensure that the platform can perform effective data interaction with various types of terminal devices.
Based on this, on the one hand, device compatibility standards are formulated: in order to improve the compatibility of the devices, corresponding device compatibility standards may be formulated. These standards may specify the communication protocols, data formats, interface types, etc. that various vehicle-mounted terminal devices need to support to ensure that they are able to interact well with the security monitoring platform.
In one aspect, a device driver and software development kit is provided: in order to enable various in-vehicle terminal devices to communicate with the security monitoring platform, corresponding device drivers and software development kits may be provided. These kits may provide standard interfaces and functions to enable the device to communicate and control data with the platform.
On the one hand, periodic updates and upgrades: in order to solve the compatibility problem of the vehicle-mounted terminal equipment, the safety monitoring platform can update and upgrade own software and hardware regularly. This ensures that the platform always supports the latest devices and communication protocols and is able to interact well with other devices.
It can be seen that real-time data acquisition and transmission: ensuring the real-time performance of traffic data requires ensuring efficient data acquisition and transmission mechanisms and reliable data transmission protocols. Data analysis and intelligent decision: the collected data is analyzed by utilizing technologies such as big data analysis, machine learning and the like, so that the functions of intelligent traffic signal control, congestion prediction, accident early warning and the like are realized. Security and privacy protection: the safe storage and transmission of the data are ensured, and the privacy of the user is protected by adopting measures such as encryption, authority control and the like, so that the data are prevented from being revealed and unauthorized access is prevented. Network stabilization and fault tolerance mechanisms: the network stability and fault tolerance mechanism is realized, the normal operation of the platform in an unstable network environment is ensured, and the continuity and stability of traffic monitoring are ensured. Multisource data integration: information from different data sources, including video monitoring, sensor data, geographic information, etc., is integrated to enable comprehensive, multidimensional traffic information monitoring and analysis.
It should be noted that, although the embodiment of the present application is described with reference to fig. 1 to sequentially describe steps S101 to S106, this does not represent that steps S101 to S106 must be performed in strict order. The steps S101 to S106 are sequentially described according to the sequence shown in fig. 1 in the embodiment of the present application, so as to facilitate the understanding of the technical solution of the embodiment of the present application by those skilled in the art. In other words, in the embodiment of the present application, the sequence between the steps S101 to S106 may be appropriately adjusted according to the actual needs.
By the method of fig. 1, vehicle information can be acquired by using the vehicle identification neural network model, and under the condition that the traffic flow of the road area is relatively high, a temporary signal lamp is searched from the signal lamp conversion period mapping table to shorten the conversion period, so that the time of the signal lamp conversion period can be automatically adjusted under the condition that the traffic flow is high, the traffic flow of the current time can be optimized, and the traffic efficiency of the vehicle is increased. And the time difference between the vehicle flow and the preset flow threshold value is controlled to control the temporary signal lamp to shorten the execution time of the transformation period, so that the traffic in the short-term future time length of the vehicle can be optimized by more accurately considering the condition of the vehicle flow in the short-term future time length of the road area, and the traffic efficiency of the road area is further improved. Thereby improving the road traffic safety monitoring efficiency.
Based on the same thought, some embodiments of the present application further provide a device and a non-volatile computer storage medium corresponding to the above method.
Fig. 2 is a schematic structural diagram of a road traffic safety monitoring device according to an embodiment of the present application, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a road image of a road area in a preset period;
outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate;
judging whether the vehicle flow exceeds a preset flow threshold;
if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period;
Determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold;
and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
Some embodiments of the present application provide a road traffic safety monitoring non-volatile computer storage medium storing computer-executable instructions configured to:
acquiring a road image of a road area in a preset period;
outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate;
judging whether the vehicle flow exceeds a preset flow threshold;
if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period;
Determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold;
and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that 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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the technical principles of the present application should fall within the protection scope of the present application.
Claims (10)
1. A method of road traffic safety monitoring, the method comprising:
acquiring a road image of a road area in a preset period;
outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate;
Judging whether the vehicle flow exceeds a preset flow threshold;
if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period;
determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold;
and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
2. The method according to claim 1, wherein said determining a first control time for shortening a transition period of said temporary signal based on a difference between said vehicle flow and said preset flow threshold, comprises:
acquiring historical vehicle information of the road area in a preset historical time length;
generating a vehicle flow trend graph of the road area every day according to the historical vehicle information;
predicting the current day vehicle flow of the road area after the current time according to the vehicle flow trend graph;
Determining a vehicle flow prediction time when the predicted vehicle flow is lower than the preset flow threshold value from the current time;
and determining the difference value between the predicted time of the vehicle flow and the current time as a first control time for shortening a conversion period of the temporary signal lamp.
3. The method according to claim 1, wherein the method further comprises:
determining an average vehicle speed for the road area based on the vehicle speed for each vehicle in the road area;
if the vehicle flow does not exceed the preset flow threshold, judging whether the average speed of the vehicle is smaller than a preset speed threshold;
if yes, matching the average speed of the vehicle in the signal lamp conversion period mapping table to obtain a temporary signal lamp increase conversion period corresponding to the average speed of the vehicle;
determining a second control time of the temporary signal lamp increase conversion period according to a difference value between the average speed of the vehicle and the preset speed threshold;
and in the second control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp increase conversion period.
4. A method according to claim 3, wherein said determining a second control time for said turn-on-turn cycle of said winker based on a difference between said average speed of said vehicle and said preset speed threshold value comprises:
Generating a vehicle average speed trend graph of the road area every day according to the historical vehicle information of the road area within a preset historical time period;
predicting the average speed of the vehicle on the current day after the current time of the road area according to the average speed trend graph of the vehicle;
starting from the current time, determining a vehicle average speed prediction time when the predicted vehicle average speed is higher than the preset speed threshold value;
and determining a difference between the vehicle average speed predicted time and the current time as a second control time of the winker increase conversion period.
5. The method according to claim 1, wherein the method further comprises:
determining a forbidden stop area of the road area;
selecting a plurality of parked vehicles with a vehicle speed of 0 from the vehicle information;
judging whether the parked vehicles in the forbidden area exist or not according to the vehicle position of each parked vehicle;
if yes, determining the vehicle in the forbidden parking area as an illegal parking vehicle;
and sending punishment notification information to a user terminal of the illegal parking vehicle according to the vehicle license plate information of the illegal parking vehicle.
6. The method according to claim 1, wherein the method further comprises:
determining a turning-around forbidden area of the road area;
generating respective corresponding vehicle trajectories for each vehicle according to a plurality of vehicle positions of each vehicle;
judging whether vehicles in the area of forbidden turning around exist or not according to the respective corresponding vehicle track of each vehicle;
if yes, determining the vehicle in the area where the turning is forbidden as an illegal turning vehicle;
and sending punishment notification information to a user terminal of the illegal turning-around vehicle according to the vehicle license plate information of the illegal turning-around vehicle.
7. The method of claim 1, wherein after the acquiring the road image of the road area, the method further comprises:
outputting road surface condition information of the road area by inputting the road image into a pre-trained road surface condition recognition neural network model; the pavement condition information comprises ponding, icing and hollowness;
generating a road surface running dangerous grade of the road area according to the road surface state information;
and when the road surface running danger level is higher than a preset level, sending road surface running danger notification information to road early warning display equipment.
8. The method according to claim 1, wherein the acquiring a road image of a road area, in particular, comprises:
receiving encrypted road image information of a road area; the encrypted road image information is obtained by asymmetric encryption through an image shooting device;
decrypting the encrypted road image information according to a private key to obtain an initial road image of the road area;
carrying out integrity verification on the initial road image, and if the verification is successful, determining the initial road image as the road image of the road area;
and if the verification fails, sending the re-uploading road image notification information to the image equipment.
9. A road traffic safety monitoring device, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a road image of a road area in a preset period;
outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate;
Judging whether the vehicle flow exceeds a preset flow threshold;
if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period;
determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold;
and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
10. A non-volatile computer storage medium storing computer executable instructions for road traffic safety monitoring, the computer executable instructions configured to:
acquiring a road image of a road area in a preset period;
outputting vehicle information of the road area by inputting the road image into a pre-trained vehicle recognition neural network model; the vehicle information comprises vehicle flow, vehicle speed, vehicle position and vehicle license plate;
Judging whether the vehicle flow exceeds a preset flow threshold;
if yes, matching the vehicle flow in a signal lamp conversion period mapping table of the road area, and obtaining a temporary signal lamp shortening conversion period corresponding to the vehicle flow; the larger the vehicle flow is, the shorter the temporary signal lamp shortens the conversion period;
determining a first control time of the temporary signal lamp for shortening a conversion period according to a difference value between the vehicle flow and the preset flow threshold;
and in the first control time, adjusting the signal lamp conversion period of the road area to the temporary signal lamp shortening conversion period.
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