CN116305735B - Computer data remote management system and method based on Internet of things - Google Patents
Computer data remote management system and method based on Internet of things Download PDFInfo
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Abstract
The invention discloses a computer data remote management system and a method based on the Internet of things, and relates to the technical field of computer data remote management. The system comprises a data acquisition module, an intelligent monitoring module, a model construction analysis module, a probability measurement module and an intelligent management module; the output end of the data acquisition module is connected with the input end of the model construction analysis module; the output end of the intelligent monitoring module is connected with the input end of the model construction analysis module; the output end of the model construction analysis module is connected with the input end of the probability measurement module; the output end of the probability measuring and calculating module is connected with the input end of the intelligent management module; the invention also provides a computer data remote management method based on the Internet of things, which is used for analyzing the running speed, the low-speed running distance, the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicles entering the ETC channel and feeding back the passing vehicles with card rubbing behaviors.
Description
Technical Field
The invention relates to the technical field of computer data remote management, in particular to a computer data remote management system and a computer data remote management method based on the Internet of things.
Background
ETC, the electronic toll collection system of the whole name, is a kind of automatic toll collection system of no-stop developed internationally for expressway or busy bridge tunnel, through the special short-range communication carried on between microwave antenna on ETC lane of the toll station and the vehicle electronic tag installed on the vehicle windshield, utilize the computer networking technology to carry on the background settlement with the bank, thus achieve the vehicle and pass the expressway or bridge toll station and need not to park and can pay the goal of the expressway or bridge cost.
With the recent economic development of China and the tremendous popularization and application of ETC by China, ETC has been spread over multiple cities, or even nationwide cities. ETC system relates to vehicle, road, toll station etc. and upstream equipment products mainly relate to vehicle identification, vehicle weighing, central management system, auxiliary facilities etc. and midstream is various integrated systems. However, in real life, there is often an ETC channel on a highway, and a user who does not transact an ETC card can choose to stop in front of or beside a user vehicle with the ETC card in order to pass through a toll gate quickly, wait for the ETC signal acquisition device to recognize the user vehicle information with the ETC card, quickly enter the ETC channel by utilizing the position advantage and do not need to pay fees, and such actions not only bring great trouble to the user with the card but also disturb the fee paying order of the highway.
Disclosure of Invention
The invention aims to provide a computer data remote management system and a method thereof based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a method for remotely managing computer data based on the internet of things, the method comprising the following steps:
step S1: acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, acquiring the current low-speed driving distance of the passing vehicle, constructing a single-lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a single lane card;
step S2: acquiring an image of a passing vehicle entering an ETC channel, acquiring a front wheel deflection angle and a vehicle body deflection displacement of the passing vehicle, constructing a neighboring lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a neighboring lane card;
step S3: establishing a probability influence factor, constructing a traffic vehicle card-rubbing probability prediction model, and calculating a predicted value of the traffic vehicle card-rubbing probability;
step S4: and constructing a remote intelligent management platform, setting a threshold value of the card rubbing probability of the passing vehicle, marking the passing vehicle when the predicted value of the card rubbing probability of the passing vehicle exceeds the threshold value, and feeding back license plate information to the manual port.
Further, in step S1, the constructing a single-lane card-rubbing probability model includes:
acquiring historical driving data of passing vehicles entering an ETC channel; the historical driving data comprise historical driving speed data of ETC channel passing vehicles and historical low-speed driving distance data of ETC channel passing vehicles;
acquiring historical driving speed data of passing vehicles entering an ETC channel, wherein the historical driving speed data is recorded as V= { V 1 、v 2 、v 3 、.......、v n}； wherein ,v1 、v 2 、v 3 、.......、v n Respectively representing the historical travel speeds of the 1 st, 2 nd, 3 rd and third-party vehicles entering the ETC lane;
according to the formula:
wherein ,representing an average travel speed of the passing vehicle into the ETC passage; v i Representing a historical travel speed of an ith passing vehicle entering the ETC passage;
acquiring historical low-speed driving distance data of passing vehicles entering an ETC channel, and recording the data as S= { S 1 、s 2 、s 3 、.......、s n}； wherein ,s1 、s 2 、s 3 、.......、s n Respectively representing the historical low-speed driving distances of the n-lane passing vehicles entering the ETC;
according to the formula:
wherein ,representing the average low-speed driving distance of the passing vehicle entering the ETC channel; s is(s) i Representing a historical low-speed driving distance of the ith passing vehicle entering the ETC channel;
collecting the current running speed of the passing vehicle at d meters from the ETC signal collecting device, and recording as v 0 ；
Collecting the current low-speed driving distance of the passing vehicle, and recording the current low-speed driving distance as s 0 ；
Constructing a single-lane card-rubbing probability model:
wherein ,P1 The probability of the passing vehicle rubbing a single lane is represented; alpha 1 An influence coefficient indicating the running speed of the passing vehicle; alpha 2 And the influence coefficient of the low-speed driving distance of the passing vehicle is shown.
In the technical scheme, considering that the passing vehicle which normally holds the card and passes through the ETC channel can be properly decelerated before entering the ETC signal acquisition area so as to ensure the accuracy of ETC information acquisition and the passing safety, for the passing vehicle which wants to rub the card and passes through the ETC channel, in order to rub the ETC card of other users, the passing vehicle which wants to rub the card and passes through the ETC channel is firstly selected to start decelerating and slowing at a position far away from the ETC channel, and the running speed is lower than the normal passing speed of the vehicle, so that after the user with the ETC card enters the ETC signal acquisition area, the passing vehicle can quickly rub the card at the first time, and the current running speed and the low-speed running distance of the passing vehicle are used as the influence factors of the probability of the passing vehicle in a single lane card, wherein the passing vehicle which wants to rub the card and passes through the ETC channel is in front of the passing vehicle which normally holds the card and passes through the ETC channel.
Further, in step S2, the constructing the adjacent lane scratch probability model includes:
collecting images of passing vehicles entering an ETC channel monitoring area; the ETC channel monitoring area is an area taking an ETC signal acquisition device as a starting point and taking an ETC signal effective identification distance as an end point;
acquiring the deflection angle of the front wheel of the passing vehicle, which is marked as theta 0 ；
Acquiring body deviation displacement of passing vehicles and marking as x 0 The method comprises the steps of carrying out a first treatment on the surface of the The vehicle body deviation displacement refers to the deviation distance between the vehicle and the middle position of the ETC channel;
constructing a neighbor lane card-rubbing probability model:
P 2 ＝β 1 *θ 0 +β 2 *x 0
wherein ,P2 The probability of the passing vehicle rubbing the card in the adjacent lane is represented; beta 1 The influence coefficient of the deflection angle of the front wheels of the passing vehicles is represented; beta 2 Indicating the coefficient of influence of the deviation displacement of the passing vehicle.
In the technical scheme, considering that the passing vehicles needing to be rubbed through the ETC channel suddenly turn from the non-ETC channel and rapidly enter the ETC channel, the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicles needing to be rubbed through the ETC channel are obviously different from those of the passing vehicles normally holding the cards and passing through the ETC channel, so that whether the passing vehicles have the rubbing action or not can be judged by collecting the images of the passing vehicles entering the monitoring area of the ETC channel and analyzing the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicles, and the accuracy of system judgment is improved, wherein the adjacent lane rubbing refers to the fact that the passing vehicles needing to be rubbed through the ETC channel normally hold the cards from the side of the passing vehicles passing through the ETC channel and enter the ETC channel.
Further, in step S3, the constructing a prediction model of the probability of the passing vehicle rubbing includes:
establishing probability influence factors, wherein the probability influence factors comprise influence coefficients of a passing vehicle on a single lane for card rubbing and influence coefficients of a passing vehicle on a temporary lane for card rubbing;
setting the influence coefficient of the passing vehicle on the single-lane card-rubbing, and marking the influence coefficient as mu 1 ；
Setting the influence coefficient of the passing vehicle on the adjacent lane card, and marking the influence coefficient as mu 2 ；
Constructing a vehicle card-rubbing probability prediction model:
P 0 ＝μ 1 *P 1 +μ 2 *P 2
wherein ,P0 A predicted value indicating a probability of a passing vehicle rubbing; p (P) 1 The probability of the passing vehicle rubbing a single lane is represented; p (P) 2 And the probability of the passing vehicle rubbing the card in the adjacent lane is indicated.
Further, in step S4, a threshold value of probability of vehicle card rub is set, denoted as P m ；
When P 0 <P m When the system is used, the passing vehicles are normally identified;
when P 0 ≥P m And when the system marks the passing vehicles and feeds license plate information back to the manual port.
In the technical scheme, considering that two ETC card-rubbing behaviors of a single lane card-rubbing and a temporary lane card-rubbing exist, the conditions of the two ETC card-rubbing behaviors are different in different ETC channels, and the times are different, so that the two ETC card-rubbing behaviors are used as influencing factors for judging the probability of the passing vehicle card-rubbing, and the accuracy of system screening judgment is further improved.
The system comprises a data acquisition module, an intelligent monitoring module, a model construction analysis module, a probability measurement module and an intelligent management module;
the data acquisition module is used for acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, and acquiring the current low-speed driving distance of the passing vehicle; the intelligent monitoring module is used for acquiring images of vehicles entering the ETC channel and acquiring the front wheel deflection angle and the vehicle body deflection displacement of the vehicles; the model construction analysis module is used for constructing a single-lane card-rubbing probability model, calculating the probability of a vehicle rubbing a single lane card, constructing a neighboring lane card-rubbing probability model and calculating the probability of the vehicle rubbing a neighboring lane card; the probability measurement module is used for establishing a probability influence factor, constructing a vehicle card-rubbing probability prediction model and calculating a predicted value of the vehicle card-rubbing probability; the intelligent management module is used for constructing a remote intelligent management platform, setting a vehicle card-rubbing probability threshold value, marking the vehicle when the predicted value of the vehicle card-rubbing probability exceeds the threshold value, and feeding license plate information back to the manual port;
the output end of the data acquisition module is connected with the input end of the model construction analysis module; the output end of the intelligent monitoring module is connected with the input end of the model construction analysis module; the output end of the model construction analysis module is connected with the input end of the probability measurement module; the output end of the probability measuring and calculating module is connected with the input end of the intelligent management module.
Further, the data acquisition module comprises a historical driving data acquisition unit and a current driving data acquisition unit;
the historical driving data acquisition unit is used for acquiring historical driving data of passing vehicles entering the ETC channel;
the current running data acquisition unit is used for acquiring the current running speed of the vehicle at the position d meters away from the ETC signal acquisition device by using a speed sensor, and acquiring the current low-speed running distance of the vehicle by using a camera device;
the output end of the historical driving data acquisition unit is connected with the input end of the current driving data acquisition unit; the output end of the current driving data acquisition unit is connected with the input end of the model construction analysis module;
the intelligent monitoring module comprises an image acquisition unit and an information extraction unit;
the image acquisition unit is used for acquiring images of passing vehicles entering the ETC channel by using the camera device;
the information extraction unit is used for acquiring the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicle;
the output end of the image acquisition unit is connected with the input end of the information extraction unit; the output end of the information extraction unit is connected with the input end of the model construction analysis module.
Further, the model construction analysis module comprises a single-lane card-rubbing probability model construction analysis unit and an adjacent-lane card-rubbing probability model construction analysis unit;
the single-lane card-rubbing probability model construction analysis unit is used for constructing a single-lane card-rubbing probability model and calculating the probability of passing vehicles on single-lane cards;
the adjacent lane card-rubbing probability model construction analysis unit is used for constructing an adjacent lane card-rubbing probability model and calculating the probability of passing vehicles rubbing the adjacent lane;
the output end of the single-lane card-rubbing probability model construction analysis unit is connected with the input end of the adjacent-lane card-rubbing probability model construction analysis unit; the output end of the adjacent lane card-rubbing probability model construction analysis unit is connected with the input end of the probability measurement module.
Further, the probability measuring and calculating module comprises a probability prediction model construction unit and a probability measuring and calculating analysis unit;
the probability prediction model construction unit is used for establishing probability influence factors and constructing a traffic vehicle card-rubbing probability prediction model;
the probability measurement and analysis unit is used for calculating a predicted value of the card rubbing probability of the passing vehicle;
the output end of the probability prediction model construction unit is connected with the input end of the probability calculation analysis unit; the output end of the probability measuring and calculating analysis unit is connected with the input end of the intelligent management module.
Further, the intelligent management unit comprises a remote intelligent management platform construction unit, a threshold setting unit and a management feedback unit;
the remote intelligent management platform construction unit is used for constructing a remote intelligent management platform;
the threshold setting unit is used for setting a threshold of the probability of card rubbing of the passing vehicle;
the management feedback unit is used for marking the passing vehicles and feeding license plate information back to the manual port when the predicted value of the card rubbing probability of the passing vehicles exceeds a threshold value;
the output end of the remote intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the management feedback unit.
Compared with the prior art, the invention has the following beneficial effects: the invention can analyze the running speed, the low-speed running distance, the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicles entering the ETC channel, and screen and feed back the passing vehicles with the card-rubbing action by constructing a single-lane card-rubbing probability model, an adjacent-lane card-rubbing probability model and a passing vehicle card-rubbing probability prediction model, so that more vehicles needing card-rubbing to pass through the ETC channel can be screened and judged to a certain extent, the property benefit safety of card-holding users is ensured, and the payment order of highways is maintained.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a computer data remote management system based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions:
a method for remotely managing computer data based on the internet of things, the method comprising the following steps:
step S1: acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, acquiring the current low-speed driving distance of the passing vehicle, constructing a single-lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a single lane card;
step S2: acquiring an image of a passing vehicle entering an ETC channel, acquiring a front wheel deflection angle and a vehicle body deflection displacement of the passing vehicle, constructing a neighboring lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a neighboring lane card;
step S3: establishing a probability influence factor, constructing a traffic vehicle card-rubbing probability prediction model, and calculating a predicted value of the traffic vehicle card-rubbing probability;
step S4: and constructing a remote intelligent management platform, setting a threshold value of the card rubbing probability of the passing vehicle, marking the passing vehicle when the predicted value of the card rubbing probability of the passing vehicle exceeds the threshold value, and feeding back license plate information to the manual port.
Further, in step S1, the constructing a single-lane card-rubbing probability model includes:
acquiring historical driving data of passing vehicles entering an ETC channel; the historical driving data comprise historical driving speed data of ETC channel passing vehicles and historical low-speed driving distance data of ETC channel passing vehicles;
acquiring historical driving speed data of passing vehicles entering an ETC channel, wherein the historical driving speed data is recorded as V= { V 1 、v 2 、v 3 、.......、v n}； wherein ,v1 、v 2 、v 3 、.......、v n Respectively representing the historical travel speeds of the 1 st, 2 nd, 3 rd and third-party vehicles entering the ETC lane;
according to the formula:
wherein ,representing an average travel speed of the passing vehicle into the ETC passage; v i Representing a historical travel speed of an ith passing vehicle entering the ETC passage;
acquiring historical low-speed driving distance data of passing vehicles entering an ETC channel, and recording the data as S= { S 1 、s 2 、s 3 、.......、s n}； wherein ,s1 、s 2 、s 3 、.......、s n Respectively representing the historical low-speed driving distances of the n-lane passing vehicles entering the ETC;
according to the formula:
wherein ,representing the average low-speed driving distance of the passing vehicle entering the ETC channel; s is(s) i Representing a historical low-speed driving distance of the ith passing vehicle entering the ETC channel;
collecting the current running speed of the passing vehicle at d meters from the ETC signal collecting device, and recording as v 0 ；
Collecting the current low-speed driving distance of the passing vehicle, and recording the current low-speed driving distance as s 0 ；
Constructing a single-lane card-rubbing probability model:
wherein ,P1 The probability of the passing vehicle rubbing a single lane is represented; alpha 1 An influence coefficient indicating the running speed of the passing vehicle; alpha 2 And the influence coefficient of the low-speed driving distance of the passing vehicle is shown.
Further, in step S2, the constructing the adjacent lane scratch probability model includes:
collecting images of passing vehicles entering an ETC channel monitoring area; the ETC channel monitoring area is an area taking an ETC signal acquisition device as a starting point and taking an ETC signal effective identification distance as an end point;
acquiring the deflection angle of the front wheel of the passing vehicle, which is marked as theta 0 ；
Acquiring body deviation displacement of passing vehicles and marking as x 0 The method comprises the steps of carrying out a first treatment on the surface of the The vehicle body deviation displacement refers to the deviation distance between the vehicle and the middle position of the ETC channel;
constructing a neighbor lane card-rubbing probability model:
P 2 ＝β 1 *θ 0 +β 2 *x 0
wherein ,P2 Representation generalProbability of a traveling vehicle rubbing a card in a neighboring lane; beta 1 The influence coefficient of the deflection angle of the front wheels of the passing vehicles is represented; beta 2 Indicating the coefficient of influence of the deviation displacement of the passing vehicle.
Further, in step S3, the constructing a prediction model of the probability of the passing vehicle rubbing includes:
establishing probability influence factors, wherein the probability influence factors comprise influence coefficients of a passing vehicle on a single lane for card rubbing and influence coefficients of a passing vehicle on a temporary lane for card rubbing;
setting the influence coefficient of the passing vehicle on the single-lane card-rubbing, and marking the influence coefficient as mu 1 ；
Setting the influence coefficient of the passing vehicle on the adjacent lane card, and marking the influence coefficient as mu 2 ；
Constructing a vehicle card-rubbing probability prediction model:
P 0 ＝μ 1 *P 1 +μ 2 *P 2
wherein ,P0 A predicted value indicating a probability of a passing vehicle rubbing; p (P) 1 The probability of the passing vehicle rubbing a single lane is represented; p (P) 2 And the probability of the passing vehicle rubbing the card in the adjacent lane is indicated.
Further, in step S4, a threshold value of probability of vehicle card rub is set, denoted as P m ；
When P 0 <P m When the system is used, the passing vehicles are normally identified;
when P 0 ≥P m And when the system marks the passing vehicles and feeds license plate information back to the manual port.
The system comprises a data acquisition module, an intelligent monitoring module, a model construction analysis module, a probability measurement module and an intelligent management module;
the data acquisition module is used for acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, and acquiring the current low-speed driving distance of the passing vehicle; the intelligent monitoring module is used for acquiring images of vehicles entering the ETC channel and acquiring the front wheel deflection angle and the vehicle body deflection displacement of the vehicles; the model construction analysis module is used for constructing a single-lane card-rubbing probability model, calculating the probability of a vehicle rubbing a single lane card, constructing a neighboring lane card-rubbing probability model and calculating the probability of the vehicle rubbing a neighboring lane card; the probability measurement module is used for establishing a probability influence factor, constructing a vehicle card-rubbing probability prediction model and calculating a predicted value of the vehicle card-rubbing probability; the intelligent management module is used for constructing a remote intelligent management platform, setting a vehicle card-rubbing probability threshold value, marking the vehicle when the predicted value of the vehicle card-rubbing probability exceeds the threshold value, and feeding license plate information back to the manual port;
the output end of the data acquisition module is connected with the input end of the model construction analysis module; the output end of the intelligent monitoring module is connected with the input end of the model construction analysis module; the output end of the model construction analysis module is connected with the input end of the probability measurement module; the output end of the probability measuring and calculating module is connected with the input end of the intelligent management module.
Further, the data acquisition module comprises a historical driving data acquisition unit and a current driving data acquisition unit;
the historical driving data acquisition unit is used for acquiring historical driving data of passing vehicles entering the ETC channel;
the current running data acquisition unit is used for acquiring the current running speed of the vehicle at the position d meters away from the ETC signal acquisition device by using a speed sensor, and acquiring the current low-speed running distance of the vehicle by using a camera device;
the output end of the historical driving data acquisition unit is connected with the input end of the current driving data acquisition unit; the output end of the current driving data acquisition unit is connected with the input end of the model construction analysis module;
the intelligent monitoring module comprises an image acquisition unit and an information extraction unit;
the image acquisition unit is used for acquiring images of passing vehicles entering the ETC channel by using the camera device;
the information extraction unit is used for acquiring the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicle;
the output end of the image acquisition unit is connected with the input end of the information extraction unit; the output end of the information extraction unit is connected with the input end of the model construction analysis module.
Further, the model construction analysis module comprises a single-lane card-rubbing probability model construction analysis unit and an adjacent-lane card-rubbing probability model construction analysis unit;
the single-lane card-rubbing probability model construction analysis unit is used for constructing a single-lane card-rubbing probability model and calculating the probability of passing vehicles on single-lane cards;
the adjacent lane card-rubbing probability model construction analysis unit is used for constructing an adjacent lane card-rubbing probability model and calculating the probability of passing vehicles rubbing the adjacent lane;
the output end of the single-lane card-rubbing probability model construction analysis unit is connected with the input end of the adjacent-lane card-rubbing probability model construction analysis unit; the output end of the adjacent lane card-rubbing probability model construction analysis unit is connected with the input end of the probability measurement module.
Further, the probability measuring and calculating module comprises a probability prediction model construction unit and a probability measuring and calculating analysis unit;
the probability prediction model construction unit is used for establishing probability influence factors and constructing a traffic vehicle card-rubbing probability prediction model;
the probability measurement and analysis unit is used for calculating a predicted value of the card rubbing probability of the passing vehicle;
the output end of the probability prediction model construction unit is connected with the input end of the probability calculation analysis unit; the output end of the probability measuring and calculating analysis unit is connected with the input end of the intelligent management module.
Further, the intelligent management unit comprises a remote intelligent management platform construction unit, a threshold setting unit and a management feedback unit;
the remote intelligent management platform construction unit is used for constructing a remote intelligent management platform;
the threshold setting unit is used for setting a threshold of the probability of card rubbing of the passing vehicle;
the management feedback unit is used for marking the passing vehicles and feeding license plate information back to the manual port when the predicted value of the card rubbing probability of the passing vehicles exceeds a threshold value;
the output end of the remote intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the management feedback unit.
In this embodiment:
acquiring historical driving data of passing vehicles entering an ETC channel; the historical driving data comprise historical driving speed data of ETC channel passing vehicles and historical low-speed driving distance data of ETC channel passing vehicles;
acquiring historical driving speed data of passing vehicles entering an ETC channel, wherein the historical driving speed data is recorded as V= { V 1 、v 2 、v 3 、.......、v n}； wherein ,v1 、v 2 、v 3 、.......、v n Respectively representing the historical travel speeds of the 1 st, 2 nd, 3 rd and third-party vehicles entering the ETC lane;
according to the formula:
wherein ,representing an average travel speed of the passing vehicle into the ETC passage; v i Representing a historical travel speed of an ith passing vehicle entering the ETC passage;
acquiring historical low-speed driving distance data of passing vehicles entering an ETC channel, and recording the data as S= { S 1 、s 2 、s 3 、.......、s n}； wherein ,s1 、s 2 、s 3 、.......、s n Respectively representing 1 st, 2 nd, 3 rd and thirdHistorical low-speed driving distance of ETC;
according to the formula:
wherein ,representing the average low-speed driving distance of the passing vehicle entering the ETC channel; s is(s) i Representing a historical low-speed driving distance of the ith passing vehicle entering the ETC channel;
according to the above formula, the average running speed of the passing vehicle entering the ETC channel
Average low speed distance travelled by passing vehicle into ETC tunnel
Collecting the current running speed v of the passing vehicle at a distance d=100 meters from the ETC signal collecting device 0 ＝18(km/h)；
Collecting the current low-speed driving distance s of passing vehicles 0 ＝200；
Setting an influence coefficient alpha of the running speed of a passing vehicle 1 ＝0.6；
Setting an influence coefficient alpha of a low-speed driving distance of a passing vehicle 2 ＝0.4；
Constructing a single-lane card-rubbing probability model:
wherein ,P1 The probability of the passing vehicle rubbing a single lane is represented; alpha 1 An influence coefficient indicating the running speed of the passing vehicle; alpha 2 An influence coefficient indicating a low-speed travel distance of the passing vehicle;
from the above formula, P 1 ＝0.6*(30-18)+0.4*(200-100)＝47.2％
Collecting images of passing vehicles entering an ETC channel monitoring area; the ETC channel monitoring area is an area taking an ETC signal acquisition device as a starting point and taking an ETC signal effective identification distance as an end point;
acquiring a front wheel yaw angle θ of a passing vehicle 0 ＝60；
Obtaining body offset displacement x of passing vehicle 0 =3; the vehicle body deviation displacement refers to the deviation distance between the vehicle and the middle position of the ETC channel;
setting an influence coefficient beta of a deflection angle of a front wheel of a passing vehicle 1 ＝0.7；
Setting the influence coefficient beta of the deviation displacement of the passing vehicle 2 ＝0.3；
Constructing a neighbor lane card-rubbing probability model:
P 2 ＝β 1 *θ 0 +β 2 *x 0
wherein ,P2 The probability of the passing vehicle rubbing the card in the adjacent lane is represented; beta 1 The influence coefficient of the deflection angle of the front wheels of the passing vehicles is represented; beta 2 Indicating the coefficient of influence of the deviation displacement of the passing vehicle.
From the above formula, P 2 ＝0.7*60+0.3*3＝42.9％
Further, in step S3, the constructing a prediction model of the probability of the passing vehicle rubbing includes:
establishing probability influence factors, wherein the probability influence factors comprise influence coefficients of a passing vehicle on a single lane for card rubbing and influence coefficients of a passing vehicle on a temporary lane for card rubbing;
setting an influence coefficient mu of a passing vehicle on a single-lane card 1 ＝0.4；
Setting an influence coefficient mu of a passing vehicle on a temporary lane card 2 ＝0.6；
Constructing a vehicle card-rubbing probability prediction model:
P 0 ＝μ 1 *P 1 +μ 2 *P 2
wherein ,P0 A predicted value indicating a probability of a passing vehicle rubbing; p (P) 1 The probability of the passing vehicle rubbing a single lane is represented; p (P) 2 And the probability of the passing vehicle rubbing the card in the adjacent lane is indicated.
From the above formula, P 0 ＝0.4*47.2+0.6*42.9＝44.62％
Setting a threshold value P of probability of vehicle card rubbing m ＝40；
Because of P 0 ≥P m Therefore, the system marks the passing vehicles and feeds license plate information back to the manual port.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The remote management method for the computer data based on the Internet of things is characterized by comprising the following steps of:
step S1: acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, acquiring the current low-speed driving distance of the passing vehicle, constructing a single-lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a single lane card;
step S2: acquiring an image of a passing vehicle entering an ETC channel, acquiring a front wheel deflection angle and a vehicle body deflection displacement of the passing vehicle, constructing a neighboring lane card-rubbing probability model, and calculating the probability of the passing vehicle rubbing a neighboring lane card;
step S3: establishing a probability influence factor, constructing a traffic vehicle card-rubbing probability prediction model, and calculating a predicted value of the traffic vehicle card-rubbing probability;
step S4: constructing a remote intelligent management platform, setting a threshold value of the card rubbing probability of the passing vehicle, marking the passing vehicle when the predicted value of the card rubbing probability of the passing vehicle exceeds the threshold value, and feeding back license plate information to the manual port;
the constructing of the single-lane card-rubbing probability model comprises the following steps:
acquiring historical driving data of passing vehicles entering an ETC channel; the historical driving data comprise historical driving speed data of ETC channel passing vehicles and historical low-speed driving distance data of ETC channel passing vehicles;
acquiring historical driving speed data of passing vehicles entering an ETC channel, wherein the historical driving speed data is recorded as V= { V 1 、v 2 、v 3 、.......、v n}； wherein ,v1 、v 2 、v 3 、.......、v n Respectively representing the historical travel speeds of the 1 st, 2 nd, 3 rd and third-party vehicles entering the ETC lane;
according to the formula:
wherein ,representing an average travel speed of the passing vehicle into the ETC passage; v i Calendar indicating entry of ith passing vehicle into ETC channelHistory of travel speed;
acquiring historical low-speed driving distance data of passing vehicles entering an ETC channel, and recording the data as S= { S 1 、s 2 、s 3 、.......、s n}； wherein ,s1 、s 2 、s 3 、.......、s n Respectively representing the historical low-speed driving distances of the n-lane passing vehicles entering the ETC;
according to the formula:
wherein ,representing the average low-speed driving distance of the passing vehicle entering the ETC channel; s is(s) i Representing a historical low-speed driving distance of the ith passing vehicle entering the ETC channel;
collecting the current running speed of the passing vehicle at d meters from the ETC signal collecting device, and recording as v 0 ；
Collecting the current low-speed driving distance of the passing vehicle, and recording the current low-speed driving distance as s 0 ；
Constructing a single-lane card-rubbing probability model:
wherein ,P1 The probability of the passing vehicle rubbing a single lane is represented; alpha 1 An influence coefficient indicating the running speed of the passing vehicle; alpha 2 An influence coefficient indicating a low-speed travel distance of the passing vehicle;
the constructing the adjacent lane card-rubbing probability model comprises the following steps:
collecting images of passing vehicles entering an ETC channel monitoring area; the ETC channel monitoring area is an area taking an ETC signal acquisition device as a starting point and taking an ETC signal effective identification distance as an end point;
acquiring the deflection angle of the front wheel of the passing vehicle, which is marked as theta 0 ；
Acquiring body deviation displacement of passing vehicles and marking as x 0 The method comprises the steps of carrying out a first treatment on the surface of the The vehicle body deviation displacement refers to the deviation distance between the vehicle and the middle position of the ETC channel;
constructing a neighbor lane card-rubbing probability model:
P 2 ＝β 1 *θ 0 +β 2 *x 0
wherein ,P2 The probability of the passing vehicle rubbing the card in the adjacent lane is represented; beta 1 The influence coefficient of the deflection angle of the front wheels of the passing vehicles is represented; beta 2 An influence coefficient indicating the deviation displacement of the passing vehicle;
the constructing the traffic vehicle card-rubbing probability prediction model comprises the following steps:
establishing probability influence factors, wherein the probability influence factors comprise influence coefficients of a passing vehicle on a single lane for card rubbing and influence coefficients of a passing vehicle on a temporary lane for card rubbing;
setting the influence coefficient of the passing vehicle on the single-lane card-rubbing, and marking the influence coefficient as mu 1 ；
Setting the influence coefficient of the passing vehicle on the adjacent lane card, and marking the influence coefficient as mu 2 ；
Constructing a vehicle card-rubbing probability prediction model:
P 0 ＝μ 1 *P 1 +μ 2 *P 2
wherein ,P0 A predicted value indicating a probability of a passing vehicle rubbing; p (P) 1 The probability of the passing vehicle rubbing a single lane is represented; p (P) 2 And the probability of the passing vehicle rubbing the card in the adjacent lane is indicated.
2. The method for remotely managing computer data based on the internet of things according to claim 1, wherein the method comprises the following steps: in step S4, a threshold value of probability of vehicle card rub is set and denoted as P m ；
When P 0 <P m When the system is used, the passing vehicles are normally identified;
when P 0 ≥P m When the system marks the passing vehicles and letters the license plateThe information is fed back to the manual port.
3. The computer data remote management system based on the internet of things applied to the computer data remote management method based on the internet of things as claimed in claim 1, which is characterized in that: the system comprises a data acquisition module, an intelligent monitoring module, a model construction analysis module, a probability measurement module and an intelligent management module;
the data acquisition module is used for acquiring historical driving data of the passing vehicle entering the ETC channel, acquiring the current driving speed of the passing vehicle at a position d meters away from the ETC signal acquisition device, and acquiring the current low-speed driving distance of the passing vehicle; the intelligent monitoring module is used for acquiring images of vehicles entering the ETC channel and acquiring the front wheel deflection angle and the vehicle body deflection displacement of the vehicles; the model construction analysis module is used for constructing a single-lane card-rubbing probability model, calculating the probability of a vehicle rubbing a single lane card, constructing a neighboring lane card-rubbing probability model and calculating the probability of the vehicle rubbing a neighboring lane card; the probability measurement module is used for establishing a probability influence factor, constructing a vehicle card-rubbing probability prediction model and calculating a predicted value of the vehicle card-rubbing probability; the intelligent management module is used for constructing a remote intelligent management platform, setting a vehicle card-rubbing probability threshold value, marking the vehicle when the predicted value of the vehicle card-rubbing probability exceeds the threshold value, and feeding license plate information back to the manual port;
the output end of the data acquisition module is connected with the input end of the model construction analysis module; the output end of the intelligent monitoring module is connected with the input end of the model construction analysis module; the output end of the model construction analysis module is connected with the input end of the probability measurement module; the output end of the probability measuring and calculating module is connected with the input end of the intelligent management module.
4. The remote management system for computer data based on the internet of things according to claim 3, wherein: the data acquisition module comprises a historical driving data acquisition unit and a current driving data acquisition unit;
the historical driving data acquisition unit is used for acquiring historical driving data of passing vehicles entering the ETC channel;
the current running data acquisition unit is used for acquiring the current running speed of the vehicle at the position d meters away from the ETC signal acquisition device by using a speed sensor, and acquiring the current low-speed running distance of the vehicle by using a camera device;
the output end of the historical driving data acquisition unit is connected with the input end of the current driving data acquisition unit; the output end of the current driving data acquisition unit is connected with the input end of the model construction analysis module;
the intelligent monitoring module comprises an image acquisition unit and an information extraction unit;
the image acquisition unit is used for acquiring images of passing vehicles entering the ETC channel by using the camera device;
the information extraction unit is used for acquiring the front wheel deflection angle and the vehicle body deflection displacement of the passing vehicle;
the output end of the image acquisition unit is connected with the input end of the information extraction unit; the output end of the information extraction unit is connected with the input end of the model construction analysis module.
5. The remote management system for computer data based on the internet of things according to claim 4, wherein: the model construction analysis module comprises a single-lane card-twitter probability model construction analysis unit and an adjacent-lane card-twitter probability model construction analysis unit;
the single-lane card-rubbing probability model construction analysis unit is used for constructing a single-lane card-rubbing probability model and calculating the probability of passing vehicles on single-lane cards;
the adjacent lane card-rubbing probability model construction analysis unit is used for constructing an adjacent lane card-rubbing probability model and calculating the probability of passing vehicles rubbing the adjacent lane;
the output end of the single-lane card-rubbing probability model construction analysis unit is connected with the input end of the adjacent-lane card-rubbing probability model construction analysis unit; the output end of the adjacent lane card-rubbing probability model construction analysis unit is connected with the input end of the probability measurement module.
6. The remote management system for computer data based on the internet of things according to claim 5, wherein: the probability measuring and calculating module comprises a probability prediction model construction unit and a probability measuring and calculating analysis unit;
the probability prediction model construction unit is used for establishing probability influence factors and constructing a traffic vehicle card-rubbing probability prediction model;
the probability measurement and analysis unit is used for calculating a predicted value of the card rubbing probability of the passing vehicle;
the output end of the probability prediction model construction unit is connected with the input end of the probability calculation analysis unit; the output end of the probability measuring and calculating analysis unit is connected with the input end of the intelligent management module.
7. The internet of things-based computer data remote management system of claim 6, wherein: the intelligent management unit comprises a remote intelligent management platform construction unit, a threshold setting unit and a management feedback unit;
the remote intelligent management platform construction unit is used for constructing a remote intelligent management platform;
the threshold setting unit is used for setting a threshold of the probability of card rubbing of the passing vehicle;
the management feedback unit is used for marking the passing vehicles and feeding license plate information back to the manual port when the predicted value of the card rubbing probability of the passing vehicles exceeds a threshold value;
the output end of the remote intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the management feedback unit.
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