CN117292504A - Traffic safety monitoring method, device, equipment and medium - Google Patents

Traffic safety monitoring method, device, equipment and medium Download PDF

Info

Publication number
CN117292504A
CN117292504A CN202311497660.0A CN202311497660A CN117292504A CN 117292504 A CN117292504 A CN 117292504A CN 202311497660 A CN202311497660 A CN 202311497660A CN 117292504 A CN117292504 A CN 117292504A
Authority
CN
China
Prior art keywords
driver
behavior
driving
information
preset
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202311497660.0A
Other languages
Chinese (zh)
Other versions
CN117292504B (en
Inventor
刘全
郭晗
葛辉
蒲天鹏
顾莉兰
苏庆龙
周煦原
马荣荣
李皓
李俊卓
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kerens Tianjin Rail Transit Technology Co ltd
Original Assignee
Kerens Tianjin Rail Transit Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Kerens Tianjin Rail Transit Technology Co ltd filed Critical Kerens Tianjin Rail Transit Technology Co ltd
Priority to CN202311497660.0A priority Critical patent/CN117292504B/en
Publication of CN117292504A publication Critical patent/CN117292504A/en
Application granted granted Critical
Publication of CN117292504B publication Critical patent/CN117292504B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61C—LOCOMOTIVES; MOTOR RAILCARS
    • B61C17/00—Arrangement or disposition of parts; Details or accessories not otherwise provided for; Use of control gear and control systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/24—Reminder alarms, e.g. anti-loss alarms

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Traffic Control Systems (AREA)

Abstract

The application relates to a traffic safety monitoring method, a device, equipment and a medium, which are applied to the technical field of rail transit, and the method comprises the following steps: acquiring a monitoring video; determining first attribute information of a current driver based on the monitoring video, wherein the first attribute information comprises behavior grade and behavior habit information of the current driver; formulating a journey reminding strategy based on the first attribute information of the current driver; and reminding the current driver in the driving process based on the journey reminding strategy. The method has the advantages that a driver can conveniently adjust the behavior in the driving process in time, and the safety in the driving process of the vehicle is improved.

Description

Traffic safety monitoring method, device, equipment and medium
Technical Field
The present disclosure relates to the technical field of rail traffic, and in particular, to a traffic safety monitoring method, device, apparatus, and medium.
Background
The rail transit has the characteristics of economy, environmental protection, large passenger flow and the like, and can effectively solve the traffic problem in the development process, however, as the rail transit system is closed, the personnel flow is large, once accidents occur, the operation is delayed when the passengers travel, and the facility system is seriously damaged when the passengers travel, so that the rail transit is paralyzed, therefore, the safety of the rail transit is not negligible, and the management and control of the safety of the rail transit are enhanced.
In rail transit, a vehicle runs along a fixed rail, and a driver is required to pay attention to the vehicle at all times to ensure safe running of the vehicle, however, due to personal habit factors, the driver often cannot pay attention to the vehicle for a long time, so rail transit accidents caused by the driver are numerous, at present, the behavior of the driver is usually analyzed after the accident occurs, the condition of the driver in the running process of the vehicle cannot be known timely, and the driver cannot adjust the behavior in the driving process timely.
Disclosure of Invention
In order to facilitate drivers to adjust behaviors in a driving process in time and improve safety in a vehicle driving process, the application provides a traffic safety monitoring method, a traffic safety monitoring device, traffic safety monitoring equipment and a traffic safety monitoring medium.
In a first aspect, the present application provides a traffic safety monitoring method, which adopts the following technical scheme:
a traffic safety monitoring method comprising:
acquiring a monitoring video;
determining first attribute information of a current driver based on the monitoring video, wherein the first attribute information comprises behavior grade and behavior habit information of the current driver;
formulating a journey reminding strategy based on the first attribute information of the current driver;
And reminding the current driver in the driving process based on the journey reminding strategy.
By adopting the technical scheme, the behavior grade and behavior habit information of the current driver, namely the first attribute information, are determined according to the monitoring video in the cab, the route reminding strategy is formulated according to the first attribute information of the current driver, the current driver is reminded according to the route reminding strategy, and different route reminding strategies are formulated according to different behavior grades and behavior habit information, so that the reminding of the driver is more accurate, the driver can conveniently adjust the behavior in the driving process in time, and the safety in the driving process of the vehicle is improved.
Optionally, the determining the first attribute information of the current driver based on the surveillance video includes:
intercepting a plurality of first characteristic images of the monitoring video according to a preset strategy;
extracting feature information of the plurality of first feature images to obtain at least one piece of feature information;
determining identity information of the current driver based on the at least one characteristic information, wherein the identity information comprises a name and a driving age;
searching second attribute information corresponding to the identity information of the current driver from a preset driver library;
Judging whether the second attribute information is perfect;
if the second attribute information is perfect, the second attribute information is used as the first attribute information;
if the second attribute information is imperfect, information acquisition is carried out on the current driver according to a first preset period, the second attribute information is perfected, and the step of judging whether the second attribute information is perfect is repeated.
By adopting the technical scheme, the identity information of the current driver is determined according to the monitoring video, the second attribute information of the current driver is searched from the preset driver library, if the second attribute information is perfect, the second attribute information is used as the first attribute information, and the first attribute information is determined directly through the preset driver library according to the identity information of the current driver, so that the determination process of the first attribute information is convenient and quick, and if the second attribute information is imperfect, the second attribute information is perfected, so that the data in the preset driver library is kept perfect at all times, so that the driver information of the preset driver library is more comprehensive, and further, the determination of the first attribute information is more convenient and quick.
Optionally, before the searching the second attribute information corresponding to the identity information of the current driver from the preset driver library, the method further includes:
Collecting a driving video set of all drivers, wherein the driving video set comprises all driving videos in a second preset period;
determining driving behaviors and driving habits of a corresponding driver based on the driving video set;
determining a behavior grade of the corresponding driver based on the driving behavior and driving habit;
establishing a preset driver library according to the driving behaviors and driving habits of the corresponding drivers;
and dividing the preset driver sub-libraries based on the behavior grades of the corresponding drivers to obtain preset driver sub-libraries with different behavior grades.
Through adopting above-mentioned technical scheme, confirm the action grade of driver according to the driving action and the driving habit of driver for the action grade of driver is more accurate, establishes the department's of predetermineeing according to the driving action and the driving habit of driver, and divides the department's of predetermineeing according to the action grade of driver, makes the driver in every department's of predetermineeing all possess the same action grade, is convenient for seek the driving action and the driving habit of driver according to the action grade.
Optionally, the determining the behavior level of the corresponding driver based on the driving behavior and the driving habit includes:
judging whether the driving behavior and the driving habit have dangerous behaviors or not, wherein the dangerous behaviors comprise a calling behavior, a smoking behavior and a talking behavior;
If the driving behavior and the driving habit of the corresponding driver have dangerous behaviors, determining dangerous driving coefficients of the corresponding driver based on the driving behavior and the driving habit;
determining a score for the corresponding driver based on the dangerous driving coefficient;
a behavioral level of the corresponding driver is determined based on the score.
By adopting the technical scheme, if the dangerous behavior exists in the driving behavior and the driving habit, the score of the corresponding driver is calculated according to the dangerous driving coefficient of the dangerous behavior, the behavior grade of the corresponding driver is determined according to the score, and the behavior grade of the driver is determined according to the driving behavior and the driving habit of the driver, so that the behavior grade is more accurate, and the driving condition of the driver can be reflected.
Optionally, the setting a trip reminding policy based on the first attribute information of the current driver includes:
acquiring running information and running time of the current train number, wherein the running information comprises a running route;
determining dangerous behavior information of the current driver based on the first attribute information, wherein the dangerous behavior information comprises a running route where the dangerous behavior occurs and occurrence time of the dangerous behavior;
judging whether the running route of the dangerous behavior is consistent with the running route of the current train number and/or whether the occurrence time of the dangerous behavior is within the running time;
If at least one condition exists, a distance reminding strategy is formulated based on the driving route and the driving time, wherein the distance reminding strategy comprises the step of sending out reminding information at a preset reminding position and/or sending out reminding information at a preset time point.
By adopting the technical scheme, the running route and the occurrence time of the dangerous behavior are determined according to the first attribute information, if the running route of the dangerous behavior is consistent with the running route of the current train number, the reminding information is sent out at the preset reminding position, if the occurrence time of the dangerous behavior is within the running time of the current train number, the reminding information is sent out at the preset time point, if the running route of the dangerous behavior is consistent with the running route of the current train number, and if the occurrence time of the dangerous behavior is within the running time of the current train number, the reminding information is sent out at the preset reminding position and the reminding information is sent out at the preset time point, so that the sending time of the reminding information is more accurate, a driver can timely adjust the behavior in the driving process by receiving the reminding information, and the safety of the vehicle in the running process is improved.
Optionally, after the formulating a trip reminder policy based on the first attribute information of the current driver, the method further includes:
Acquiring the number of first drivers of the current train number;
determining the number of second drivers in the cab of the current train number based on the monitoring video;
judging whether the first driver quantity is consistent with the second driver quantity;
if the number of the first drivers is consistent with the number of the second drivers, determining the behavior grades of all drivers based on the monitoring video;
judging whether the behavior grades of all drivers accord with a preset collocation strategy or not;
and if the behavior grades of all drivers do not accord with the preset collocation strategy, adjusting the route reminding strategy to obtain a new route reminding strategy.
By adopting the technical scheme, whether the number of the first drivers is consistent with the number of the second drivers is judged, whether the drivers drive according to the number of the regulated persons is judged, whether the behavior grades of all drivers in the current train number accord with the preset collocation strategy is judged, and if the behavior grades do not accord with the preset collocation strategy, the drivers are reminded, so that the drivers can timely adjust collocation conditions, and the safety in the driving process is improved.
Optionally, after the reminding the current driver in the driving process based on the journey reminding policy, the method further includes:
Acquiring a driving video of a driver of the current train number;
judging whether a driver of the current train number has a dangerous behavior or not based on the driving video;
if the dangerous behavior exists, acquiring the times of the historical dangerous behavior;
iteratively updating the times of the historical dangerous behaviors to obtain the latest historical dangerous times;
judging whether the latest historical hazard times are larger than preset hazard times or not;
if the latest historical hazard times are not greater than the preset hazard times, warning a driver of the current vehicle number;
and if the latest historical hazard times are larger than the preset hazard times, adjusting the behavior grade of the driver of the current train number.
By adopting the technical scheme, if the latest historical hazard times are greater than the preset hazard times, the behavior grade of the driver is adjusted, so that the behavior grade of the driver is more accurate, the collocation of the driver is more reasonable, and the safety in the driving process is improved.
In a second aspect, the present application provides a traffic safety monitoring device, which adopts the following technical scheme:
a traffic safety monitoring device comprising:
the video acquisition module is used for acquiring a monitoring video;
The information determining module is used for determining first attribute information of a current driver based on the monitoring video, wherein the first attribute information comprises behavior grade and behavior habit information of the current driver;
the strategy making module is used for making a journey reminding strategy based on the first attribute information of the current driver;
and the driver reminding module is used for reminding the current driver in the driving process based on the journey reminding strategy.
By adopting the technical scheme, the monitoring video is acquired through the video acquisition module, the behavior grade and behavior habit information of the current driver are determined through the information determination module, namely the first attribute information, the route reminding strategy is formulated through the strategy formulation module according to the first attribute information of the current driver, the current driver is reminded through the driver reminding module according to the route reminding strategy, different route reminding strategies are formulated according to different behavior grades and behavior habit information, the reminding of the driver is more accurate, the driver can conveniently and timely adjust the behavior in the driving process, and the safety in the vehicle driving process is improved.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
An electronic device comprising a processor coupled with a memory;
the memory has stored thereon a computer program that can be loaded by a processor and that performs the traffic safety monitoring method according to any of the first aspects.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the traffic safety monitoring method according to any one of the first aspects.
Drawings
Fig. 1 is a schematic flow chart of a traffic safety monitoring method according to an embodiment of the present application.
Fig. 2 is a block diagram of a traffic safety monitoring device 200 according to an embodiment of the present application.
Fig. 3 is a block diagram of an electronic device 300 according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a traffic safety monitoring method, which can be executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a desktop computer, etc.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
As shown in fig. 1, a traffic safety monitoring method is described as follows (steps S101 to S104):
step S101, acquiring a monitoring video.
The monitoring camera is arranged in the cab, the condition in the cab can be shot in real time, the shot monitoring video can be stored in the database, and the monitoring video is acquired from the database.
Step S102, first attribute information of a current driver is determined based on the monitoring video.
The first attribute information comprises the behavior grade and behavior habit information of the current driver, and the behavior habit information comprises driving behaviors and driving habits.
Specifically, determining the first attribute information of the current driver based on the surveillance video includes: intercepting a plurality of first characteristic images of the monitoring video according to a preset strategy; extracting feature information of the plurality of first feature images to obtain at least one piece of feature information; determining identity information of a current driver based on at least one piece of characteristic information, wherein the identity information comprises a name and driving age; searching second attribute information corresponding to the identity information of the current driver from a preset driver library; judging whether the second attribute information is perfect; if the second attribute information is perfect, the second attribute information is used as the first attribute information; if the second attribute information is imperfect, the information acquisition is carried out on the current driver according to the first preset period, the second attribute information is perfect, and the step of judging whether the second attribute information is perfect is repeated.
In this embodiment, the first feature image is a face image, the preset strategy is to intercept the face image from the monitoring video every other preset time, the preset time may be 10 minutes, or may be 20 minutes, the facial feature of the face image is extracted to obtain at least one feature information, the at least one feature information is compared with facial feature information in a facial database, the name of the current driver is determined, the driving age of the driver is searched from a driver information database according to the name of the current driver, the name and the driving age of the driver are stored in the driver information database, the second attribute information of the driver is stored in the preset driver database, the second attribute information comprises the behavior grade and behavior habit information of the driver, whether the second attribute information of the current driver is perfect or not is judged, namely whether the behavior grade and behavior habit information of the current driver are contained in the preset driver is judged, and if the behavior grade and behavior habit information of the current driver are contained in the preset driver, the second attribute information of the current driver is perfect, and the second attribute information of the current driver is regarded as the first attribute information of the current driver; if the preset driver's behavior class and/or behavior habit information of the current driver is not contained in the preset driver's library, the second attribute information of the current driver is imperfect, if the preset driver's behavior habit information of the current driver is not contained in the preset driver's library, the behavior habit information of the current driver is collected in a first preset period, the first preset period may be one month or two months, the collection mode may be collection from a driver behavior habit database, wherein the driver behavior habit database stores driving behaviors and driving habits of the driver, or collection from the driver, namely, a behavior habit information questionnaire filled by the driver is obtained, the behavior habit information questionnaire comprises driving behaviors and driving habits, the behavior habit information questionnaire is an electronic questionnaire, if the preset driver's behavior class does not contain the current driver, the behavior class of the current driver is determined based on the driving behaviors and the driving habits of the current driver, the determined behavior class and/or the collected behavior habit information of the current driver is stored in the preset driver's library, the preset driver's library is thus perfected, and whether the second attribute information is perfected or not is repeated, and the second attribute information is not perfected.
Specifically, before searching the second attribute information corresponding to the identity information of the current driver from the preset driver library, the method further comprises: collecting driving video sets of all drivers, wherein the driving video sets comprise all driving videos in a second preset period; determining driving behaviors and driving habits of a corresponding driver based on the driving video set; determining a behavior grade of a corresponding driver based on the driving behavior and the driving habit; establishing a preset driver library according to the driving behaviors and driving habits of the corresponding drivers; and dividing the preset driver libraries based on the behavior grades of the corresponding drivers to obtain preset driver sub libraries with different behavior grades.
In this embodiment, all driving videos in a second preset period are obtained from a database, where the second preset period may be one month, or may be two months, driving videos corresponding to each driver are input into a deep learning model, and driving behaviors of each driver are identified, where the driving behaviors include drinking behaviors, calling behaviors, smoking behaviors, and talking behaviors, the deep learning model may be a deep learning target detection yolov5s model, and according to the driving videos, a driving route and/or a driving time corresponding to each driving behavior are determined, and driving habits of each driving behavior and each corresponding driving route and/or driving time are analyzed, and driving habits of the driver are determined, where the driving habits are used to characterize the driving habits, that is, the number of times that any driving behavior exists by the driver is greater than a preset number of driving routes and/or driving times and corresponding driving behaviors, and the preset number of times may be 3, or 5, for example: the driving route of the driver 1 with the smoking behavior times larger than the preset times is A station-B station, and the driving habit of the driver 1 is that the driver 1 is easy to generate smoking behavior in the A station-B station; the travel time for which the driver 2 has smoking behavior more than the preset number of times is 10:00, the driving habit of the driver 2 is that the driver 2 is at 10:00 is susceptible to smoking behavior.
Determining the behavior grade of a driver according to driving behaviors and driving habits, storing the driving behaviors and the driving habits and the driver in a mutually corresponding manner to obtain a preset driver library, dividing the preset driver library according to the behavior grade of the driver to obtain at least one preset driver sub-library, wherein the drivers in each preset driver sub-library have the same behavior grade, and the behavior grades corresponding to the preset driver sub-libraries are different.
Specifically, determining the behavior level of the corresponding driver based on the driving behavior and the driving habit includes: judging whether the driving behavior and the driving habit have dangerous behaviors or not; if the driving behavior and the driving habit of the corresponding driver have dangerous behaviors, determining dangerous driving coefficients of the corresponding driver based on the driving behavior and the driving habit; determining a score for the corresponding driver based on the dangerous driving coefficient; the behavioral level of the corresponding driver is determined based on the score.
In the driving process of a driver, the behavior which needs to be considered in a distracting way or influences the driving environment is dangerous, namely, the behavior is a dangerous behavior, the dangerous behavior comprises a calling behavior, a smoking behavior and a talking behavior, if the dangerous behavior exists in the driving behavior and the driving habit of the corresponding driver, the dangerous driving coefficient of the corresponding driver is determined, wherein the dangerous driving coefficient of the calling behavior is 10, the dangerous driving coefficient of the smoking behavior is 7, the dangerous driving coefficient of the talking behavior is 3, the score of the corresponding driver is calculated according to the initial preset score, the dangerous driving coefficient and the occurrence frequency of the dangerous behavior, and the calculation mode of the score is as follows: the initial preset score-dangerous driving coefficient is the number of times of occurrence of dangerous behaviors, if the score of the corresponding driver is smaller than the first preset score, the behavior grade of the corresponding driver is a third grade, if the score of the corresponding driver is not smaller than the first preset score and smaller than the second preset score, the behavior grade of the corresponding driver is a second grade, and if the score of the corresponding driver is not smaller than the second preset score, the behavior grade of the corresponding driver is a first grade, wherein the first preset score is smaller than the second preset score, for example: if the initial preset score is 100, the first preset score is 20, the second preset score is 60, the number of times of occurrence of the corresponding calling behavior of the driver is 2, the number of times of occurrence of smoking behavior is 1, the number of times of occurrence of talking behavior is 0, the score of the corresponding driver is 100-10 x 2-7*1 =73, and the behavior grade of the corresponding driver is the first grade.
And step S103, a journey reminding strategy is formulated based on the first attribute information of the current driver.
In this embodiment, a trip reminding policy is formulated according to the behavior class and behavior habit information of the current driver.
Specifically, formulating the trip alert strategy based on the first attribute information of the current driver includes: acquiring running information and running time of the current train number, wherein the running information comprises a running route; determining hazard behavior information of a current driver based on the first attribute information, wherein the hazard behavior information comprises a driving route where the hazard behavior occurs and occurrence time of the hazard behavior; judging whether the running route of the dangerous behavior is consistent with the running route of the current train number and/or whether the occurrence time of the dangerous behavior is within the running time; if at least one condition exists, a route reminding strategy is formulated based on the driving route and the driving time, wherein the route reminding strategy comprises the steps of sending reminding information at a preset reminding position and/or sending the reminding information at a preset time point.
In this embodiment, the occurrence of the dangerous behavior is regular according to the behavior habit of the driver, that is, the occurrence time of the dangerous behavior and the driving route of the occurrence of the dangerous behavior are regular, the regularity is behavior habit information of the driver, the driving information and the driving time of the current number of vehicles are obtained from the database, the dangerous behavior information of the current driver is determined according to the behavior habit information in the first attribute information, and the behavior habit information that the driving behavior is the dangerous behavior in the behavior habit information is determined as the dangerous behavior information.
If there is at least one condition that the running route of the dangerous behavior is consistent with the running route of the current train number and/or the occurrence time of the dangerous behavior is within the running time, a route reminding policy is formulated based on the running route and the running time, if the running route of the dangerous behavior is consistent with the running route of the current train number, the route reminding policy carries out voice reminding on a driver when the route of the dangerous behavior reaches a corresponding station, and if the occurrence time of the dangerous behavior is within the running time, the route reminding policy carries out voice reminding on the driver circularly from 3 minutes before the occurrence time until the occurrence time is ended, for example: if the driving route of the current train number is M station-A station-B station-C station, the driver of the current train number is the driver 1, the preset reminding position is A station, and when the path reminding strategy of the driver 1 reaches the A station, reminding information is sent to the driver, wherein the content of the reminding information is 'please pay attention to without smoking'; if the running time of the current train number is 9:00-11:00, the driver of the current train number is the driver 2, and the preset time point is 9:57-10:00, the distance reminding strategy of the driver 2 is that at 9:57-10:00 sends reminding information to the driver, wherein the content of the reminding information is 'please pay attention to not smoke'.
Specifically, after formulating the trip reminder strategy based on the first attribute information of the current driver, the method further comprises: acquiring the number of first drivers of the current train number; determining a second driver number in the cab of the current vehicle number based on the monitoring video; judging whether the number of the first drivers is consistent with the number of the second drivers; if the number of the first drivers is consistent with the number of the second drivers, determining the behavior grades of all drivers based on the monitoring video; judging whether the behavior grades of all drivers accord with a preset collocation strategy; and if the behavior grades of all drivers do not accord with the preset collocation strategy, adjusting the route reminding strategy to obtain a new route reminding strategy.
The preset collocation strategy comprises the following steps: if the number of drivers is one, the behavior grade of the drivers is a first grade or a second grade; if the number of drivers is greater than one and the behavior level of the driver is a third level, at least one driver's behavior level is a first level; if the number of drivers is greater than one and the behavior level of no driver is the third level, the behavior level of each driver may be the first level or the second level.
The method comprises the steps of obtaining the first number of drivers of the current number of vehicles, namely the number of drivers arranged in the current number of vehicles, carrying out image recognition on a monitoring video, determining the number of people in the monitoring video, namely the second number of drivers, carrying out face recognition on the monitoring video if the second number of drivers is consistent with the first number of drivers, determining identity information of the drivers in a cab, determining behavior grades of the drivers according to a preset driver bank, prompting the drivers according to a route prompting strategy if the behavior grades of all the drivers accord with the preset collocation strategy, adjusting the route prompting strategy if the behavior grades of the drivers do not accord with the preset collocation strategy, and prompting when reaching the next site on the basis of the route prompting strategy of each driver until the behavior grades of all the drivers accord with the preset collocation strategy, wherein prompting content can be "please timely adjust drivers, notice driving safety", and obtain a new route prompting strategy.
Step S104, reminding the current driver in the driving process based on the journey reminding strategy.
And reminding the current driver according to the path reminding strategy, so that the current driver timely adjusts the behavior in the driving process, and the safety of the vehicle in the driving process is improved.
Specifically, after reminding the current driver in the driving process based on the journey reminding strategy, the method further comprises the following steps: acquiring a driving video of a driver of the current train number; judging whether a driver of the current train number has a dangerous behavior or not based on the driving video; if the dangerous behavior exists, acquiring the times of the historical dangerous behavior; iteratively updating the times of the historical harmful behaviors to obtain the latest historical harmful times; judging whether the latest historical hazard times are larger than preset hazard times or not; if the latest historical hazard times are not more than the preset hazard times, warning a driver of the current train number; and if the latest historical hazard times are greater than the preset hazard times, adjusting the behavior grade of the driver of the current train number.
In this embodiment, a driving video of the current driving of the vehicle is obtained in real time, a driving behavior of the driver of the current driving of the vehicle is identified according to a behavior identification model, whether a dangerous behavior exists in the driving behavior of the driver of the current driving of the vehicle is judged, if the dangerous behavior exists, the number of times of the dangerous behavior exists in the history of the driver of the current driving of the vehicle is obtained from a preset driver library, 1 is added to the number of times of the dangerous behavior existing in the history to obtain the latest historical dangerous frequency, the latest historical dangerous frequency is compared with the preset dangerous frequency after the latest historical dangerous frequency is obtained each time, if the latest historical dangerous frequency is not greater than the preset dangerous frequency, a voice warning is carried out on the driver of the current driving of the vehicle, warning content is "please pay attention to normal driving behavior", if the latest historical dangerous frequency is greater than the preset dangerous frequency, the score of the driver of the current driving of the vehicle is recalculated, and the behavior grade of the corresponding driver is determined according to the score.
The preset hazard times are determined according to the driving age of a driver, for example: if the driving age of the driver is 20 years, the preset hazard number is 10 times, if the driving age of the driver is 10 years, the preset hazard number is 6 times, and if the driving age of the driver is 5 years, the preset hazard number is 3 times.
In addition, the physical health of the driver is also an important factor affecting the driving safety, the monitoring video is subjected to image recognition, whether the driver has abnormal states or not is judged, the abnormal states comprise an eye-closing state and a pale complexion state, if the abnormal states exist, health monitoring data are obtained, the health monitoring data comprise heart rate data and body temperature data, whether the driver has the health abnormality or not is determined based on the health monitoring data, if the abnormal states exist, the driver is reminded by voice, the voice content is' the state is bad, the driver is recommended to be replaced in time, and the driver is required to take a rest.
Specifically, the health monitoring equipment is worn by the driver, the heart rate and the body temperature of the driver are monitored in real time, health monitoring data are obtained from the health monitoring equipment, and if the heart rate of the driver is larger than the preset heart rate or the body temperature is larger than the preset temperature, the health of the driver is indicated to be problematic, and the health of the driver is abnormal.
Fig. 2 is a block diagram of a traffic safety monitoring device 200 according to an embodiment of the present application.
As shown in fig. 2, the traffic safety monitoring device 200 includes:
a video acquisition module 201, configured to acquire a monitoring video;
an information determining module 202, configured to determine first attribute information of the current driver based on the surveillance video, where the first attribute information includes behavior level and behavior habit information of the current driver;
the policy making module 203 is configured to make a trip reminding policy based on the first attribute information of the current driver;
the driver reminding module 204 reminds the current driver in the driving process based on the journey reminding strategy.
As an optional implementation manner of this embodiment, the information determining module 201 is further specifically configured to determine, based on the surveillance video, first attribute information of the current driver, including: intercepting a plurality of first characteristic images of the monitoring video according to a preset strategy; extracting feature information of the plurality of first feature images to obtain at least one piece of feature information; determining identity information of a current driver based on at least one piece of characteristic information, wherein the identity information comprises a name and driving age; searching second attribute information corresponding to the identity information of the current driver from a preset driver library; judging whether the second attribute information is perfect; if the second attribute information is perfect, the second attribute information is used as the first attribute information; if the second attribute information is imperfect, the information acquisition is carried out on the current driver according to the first preset period, the second attribute information is perfect, and the step of judging whether the second attribute information is perfect is repeated.
As an optional implementation manner of this embodiment, the traffic safety monitoring device 200 is further specifically configured to, before searching the second attribute information corresponding to the identity information of the current driver from the preset driver library, include: collecting driving video sets of all drivers, wherein the driving video sets comprise all driving videos in a second preset period; determining driving behaviors and driving habits of a corresponding driver based on the driving video set; determining a behavior grade of a corresponding driver based on the driving behavior and the driving habit; establishing a preset driver library according to the driving behaviors and driving habits of the corresponding drivers; and dividing the preset driver libraries based on the behavior grades of the corresponding drivers to obtain preset driver sub libraries with different behavior grades.
As an alternative implementation manner of the present embodiment, the information determining module 201 is further specifically configured to determine a behavior level of a corresponding driver based on driving behavior and driving habit, including: judging whether the driving behavior and the driving habit have dangerous behaviors, wherein the dangerous behaviors comprise a calling behavior, a smoking behavior and a talking behavior; if the driving behavior and the driving habit of the corresponding driver have dangerous behaviors, determining dangerous driving coefficients of the corresponding driver based on the driving behavior and the driving habit; determining a score for the corresponding driver based on the dangerous driving coefficient; the behavioral level of the corresponding driver is determined based on the score.
As an optional implementation manner of this embodiment, the policy making module 203 is further specifically configured to make a trip reminding policy based on the first attribute information of the current driver, including: acquiring running information and running time of the current train number, wherein the running information comprises a running route; determining hazard behavior information of a current driver based on the first attribute information, wherein the hazard behavior information comprises a driving route where the hazard behavior occurs and occurrence time of the hazard behavior; judging whether the running route of the dangerous behavior is consistent with the running route of the current train number and/or whether the occurrence time of the dangerous behavior is within the running time; if at least one condition exists, a route reminding strategy is formulated based on the driving route and the driving time, wherein the route reminding strategy comprises the steps of sending reminding information at a preset reminding position and/or sending the reminding information at a preset time point.
As an alternative implementation manner of this embodiment, the traffic safety monitoring device 200 is further specifically configured to, after formulating the trip alert policy based on the first attribute information of the current driver, include: acquiring the number of first drivers of the current train number; determining a second driver number in the cab of the current vehicle number based on the monitoring video; judging whether the number of the first drivers is consistent with the number of the second drivers; if the number of the first drivers is consistent with the number of the second drivers, determining the behavior grades of all drivers based on the monitoring video; judging whether the behavior grades of all drivers accord with a preset collocation strategy; and if the behavior grades of all drivers do not accord with the preset collocation strategy, adjusting the route reminding strategy to obtain a new route reminding strategy.
As an alternative implementation manner of the present embodiment, the traffic safety monitoring device 200 is further specifically configured to, after alerting the current driver during driving based on the trip alert policy, include: acquiring a driving video of a driver of the current train number; judging whether a driver of the current train number has a dangerous behavior or not based on the driving video; if the dangerous behavior exists, acquiring the times of the historical dangerous behavior; iteratively updating the times of the historical harmful behaviors to obtain the latest historical harmful times; judging whether the latest historical hazard times are larger than preset hazard times or not; if the latest historical hazard times are not more than the preset hazard times, warning a driver of the current train number; and if the latest historical hazard times are greater than the preset hazard times, adjusting the behavior grade of the driver of the current train number.
In one example, a module in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when a module in an apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and modules described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Fig. 3 is a block diagram of an electronic device 300 according to an embodiment of the present application.
As shown in FIG. 3, electronic device 300 includes a processor 301 and memory 302, and may further include an information input/information output (I/O) interface 303, one or more of a communication component 304, and a communication bus 305.
Wherein the processor 301 is configured to control the overall operation of the electronic device 300 to perform all or part of the steps of the traffic safety monitoring method described above; the memory 302 is used to store various types of data to support operation at the electronic device 300, which may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The Memory 302 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as one or more of static random access Memory (Static Random Access Memory, SRAM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The I/O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, or 4G, or a combination of one or more thereof, and accordingly the communication component 304 can include: wi-Fi part, bluetooth part, NFC part.
The electronic device 300 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (Digital Signal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the traffic safety monitoring methods as set forth in the above embodiments.
Communication bus 305 may include a pathway to transfer information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.
The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like, and may also be a server, and the like.
The application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the traffic safety monitoring method when being executed by a processor.
The computer readable storage medium may include: a U-disk, a removable hard disk, a read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
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.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.

Claims (10)

1. A traffic safety monitoring method, comprising:
acquiring a monitoring video;
determining first attribute information of a current driver based on the monitoring video, wherein the first attribute information comprises behavior grade and behavior habit information of the current driver;
Formulating a journey reminding strategy based on the first attribute information of the current driver;
and reminding the current driver in the driving process based on the journey reminding strategy.
2. The method of claim 1, wherein the determining first attribute information of the current driver based on the surveillance video comprises:
intercepting a plurality of first characteristic images of the monitoring video according to a preset strategy;
extracting feature information of the plurality of first feature images to obtain at least one piece of feature information;
determining identity information of the current driver based on the at least one characteristic information, wherein the identity information comprises a name and a driving age;
searching second attribute information corresponding to the identity information of the current driver from a preset driver library;
judging whether the second attribute information is perfect;
if the second attribute information is perfect, the second attribute information is used as the first attribute information;
if the second attribute information is imperfect, information acquisition is carried out on the current driver according to a first preset period, the second attribute information is perfected, and the step of judging whether the second attribute information is perfect is repeated.
3. The method according to claim 2, wherein before the searching the second attribute information corresponding to the identity information of the current driver from the preset driver library, the method further comprises:
collecting a driving video set of all drivers, wherein the driving video set comprises all driving videos in a second preset period;
determining driving behaviors and driving habits of a corresponding driver based on the driving video set;
determining a behavior grade of the corresponding driver based on the driving behavior and driving habit;
establishing a preset driver library according to the driving behaviors and driving habits of the corresponding drivers;
and dividing the preset driver sub-libraries based on the behavior grades of the corresponding drivers to obtain preset driver sub-libraries with different behavior grades.
4. The method of claim 3, wherein the determining the behavior level of the corresponding driver based on the driving behavior and driving habits comprises:
judging whether the driving behavior and the driving habit have dangerous behaviors or not, wherein the dangerous behaviors comprise a calling behavior, a smoking behavior and a talking behavior;
if the driving behavior and the driving habit of the corresponding driver have dangerous behaviors, determining dangerous driving coefficients of the corresponding driver based on the driving behavior and the driving habit;
Determining a score for the corresponding driver based on the dangerous driving coefficient;
a behavioral level of the corresponding driver is determined based on the score.
5. The method of claim 1, wherein the formulating a trip reminder strategy based on the first attribute information of the current driver comprises:
acquiring running information and running time of the current train number, wherein the running information comprises a running route;
determining dangerous behavior information of the current driver based on the first attribute information, wherein the dangerous behavior information comprises a running route where the dangerous behavior occurs and occurrence time of the dangerous behavior;
judging whether the running route of the dangerous behavior is consistent with the running route of the current train number and/or whether the occurrence time of the dangerous behavior is within the running time;
if at least one condition exists, a distance reminding strategy is formulated based on the driving route and the driving time, wherein the distance reminding strategy comprises the step of sending out reminding information at a preset reminding position and/or sending out reminding information at a preset time point.
6. The method of claim 1, wherein after the formulating a trip reminder strategy based on the first attribute information of the current driver, the method further comprises:
Acquiring the number of first drivers of the current train number;
determining the number of second drivers in the cab of the current train number based on the monitoring video;
judging whether the first driver quantity is consistent with the second driver quantity;
if the number of the first drivers is consistent with the number of the second drivers, determining the behavior grades of all drivers based on the monitoring video;
judging whether the behavior grades of all drivers accord with a preset collocation strategy or not;
and if the behavior grades of all drivers do not accord with the preset collocation strategy, adjusting the route reminding strategy to obtain a new route reminding strategy.
7. The method of claim 1, wherein after alerting the current driver during driving based on the course alert strategy, the method further comprises:
acquiring a driving video of a driver of the current train number;
judging whether a driver of the current train number has a dangerous behavior or not based on the driving video;
if the dangerous behavior exists, acquiring the times of the historical dangerous behavior;
iteratively updating the times of the historical dangerous behaviors to obtain the latest historical dangerous times;
Judging whether the latest historical hazard times are larger than preset hazard times or not;
if the latest historical hazard times are not greater than the preset hazard times, warning a driver of the current vehicle number;
and if the latest historical hazard times are larger than the preset hazard times, adjusting the behavior grade of the driver of the current train number.
8. A traffic safety monitoring device, comprising:
the video acquisition module is used for acquiring a monitoring video;
the information determining module is used for determining first attribute information of a current driver based on the monitoring video, wherein the first attribute information comprises behavior grade and behavior habit information of the current driver;
the strategy making module is used for making a journey reminding strategy based on the first attribute information of the current driver;
and the driver reminding module is used for reminding the current driver in the driving process based on the journey reminding strategy.
9. An electronic device comprising a processor coupled to a memory;
the processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium comprising a computer program or instructions which, when run on a computer, cause the computer to perform the method of any of claims 1 to 7.
CN202311497660.0A 2023-11-11 2023-11-11 Traffic safety monitoring method, device, equipment and medium Active CN117292504B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311497660.0A CN117292504B (en) 2023-11-11 2023-11-11 Traffic safety monitoring method, device, equipment and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311497660.0A CN117292504B (en) 2023-11-11 2023-11-11 Traffic safety monitoring method, device, equipment and medium

Publications (2)

Publication Number Publication Date
CN117292504A true CN117292504A (en) 2023-12-26
CN117292504B CN117292504B (en) 2024-06-11

Family

ID=89253693

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311497660.0A Active CN117292504B (en) 2023-11-11 2023-11-11 Traffic safety monitoring method, device, equipment and medium

Country Status (1)

Country Link
CN (1) CN117292504B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110406541A (en) * 2019-06-12 2019-11-05 天津五八到家科技有限公司 Driving data processing method, equipment, system and storage medium
CN112644514A (en) * 2020-12-31 2021-04-13 上海商汤临港智能科技有限公司 Driving data processing method, device, equipment, storage medium and program product
CN112734144A (en) * 2019-10-14 2021-04-30 中移智行网络科技有限公司 Driving behavior evaluation method and device, storage medium and computer equipment
KR20210063597A (en) * 2019-11-25 2021-06-02 한국자동차연구원 Apparatus and method for providng safe driving information based on history of dangerous actions
CN113619588A (en) * 2020-05-09 2021-11-09 北京地平线机器人技术研发有限公司 Driving behavior reminding method and device, storage medium and electronic equipment
CN114926824A (en) * 2022-05-17 2022-08-19 广西科技大学 Method for judging bad driving behavior
US20230048112A1 (en) * 2020-10-15 2023-02-16 Tencent Technology ( Shenzhen) Company Limited Warning method and apparatus for driving risk, computing device and storage medium
CN116959265A (en) * 2023-07-27 2023-10-27 中国联合网络通信集团有限公司 Traffic information prompting method, device, electronic equipment and readable storage medium

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110406541A (en) * 2019-06-12 2019-11-05 天津五八到家科技有限公司 Driving data processing method, equipment, system and storage medium
CN112734144A (en) * 2019-10-14 2021-04-30 中移智行网络科技有限公司 Driving behavior evaluation method and device, storage medium and computer equipment
KR20210063597A (en) * 2019-11-25 2021-06-02 한국자동차연구원 Apparatus and method for providng safe driving information based on history of dangerous actions
CN113619588A (en) * 2020-05-09 2021-11-09 北京地平线机器人技术研发有限公司 Driving behavior reminding method and device, storage medium and electronic equipment
US20230048112A1 (en) * 2020-10-15 2023-02-16 Tencent Technology ( Shenzhen) Company Limited Warning method and apparatus for driving risk, computing device and storage medium
CN112644514A (en) * 2020-12-31 2021-04-13 上海商汤临港智能科技有限公司 Driving data processing method, device, equipment, storage medium and program product
CN114926824A (en) * 2022-05-17 2022-08-19 广西科技大学 Method for judging bad driving behavior
CN116959265A (en) * 2023-07-27 2023-10-27 中国联合网络通信集团有限公司 Traffic information prompting method, device, electronic equipment and readable storage medium

Also Published As

Publication number Publication date
CN117292504B (en) 2024-06-11

Similar Documents

Publication Publication Date Title
CN111815986B (en) Traffic accident early warning method and device, terminal equipment and storage medium
US10703379B1 (en) System and methods for determining owner's preferences based on vehicle owner's telematics data
CN112863172B (en) Highway traffic running state judgment method, early warning method, device and terminal
US20140081675A1 (en) Systems, methods, and apparatus for optimizing claim appraisals
US8812352B2 (en) Environmental stewardship based on driving behavior
CN109800984B (en) Driving level evaluation method, driving level evaluation device, computer device, and storage medium
CN112530163B (en) Traffic accident prediction method, traffic accident prediction device, electronic device, and storage medium
CN109886820B (en) Accident vehicle identification method and terminal equipment
CN110599353A (en) Vehicle insurance and claims rate prediction method, device, equipment and medium
CN110132293B (en) Route recommendation method and device
CN111091215B (en) Vehicle identification method, device, computer equipment and storage medium
CN111881243B (en) Taxi track hot spot area analysis method and system
CN112102959A (en) Server, data processing method, data processing device and readable storage medium
CN113611104B (en) Risk identification method and device for freight vehicle, storage medium and terminal
CN117292504B (en) Traffic safety monitoring method, device, equipment and medium
CN113139852A (en) Overload prevention method and device for shared vehicle and electronic equipment
CN112612958A (en) Order processing method and device, electronic equipment and computer readable medium
US20190251587A1 (en) Information providing device, information providing program and information providing method
US20230058076A1 (en) Method and system for auto generating automotive data quality marker
CN111382883A (en) Network appointment vehicle order-receiving authority control method, device and equipment
CN114493250A (en) Abnormal behavior detection method, computing device and readable storage medium
CN110718087B (en) Data fusion processing method and device
CN110827152A (en) Insurance evaluation method, system and server
CN112700648A (en) Method and device for determining traffic violation position
CN112417983A (en) Vehicle driver determination method, device, equipment and medium based on multi-source data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant