CN112985436A - Logistics vehicle-mounted navigation system based on big data - Google Patents
Logistics vehicle-mounted navigation system based on big data Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3461—Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
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- G01C21/3469—Fuel consumption; Energy use; Emission aspects
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
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Abstract
The invention discloses a logistics vehicle-mounted navigation system based on big data, and relates to the field of logistics transportation; the fuel quantity monitoring system comprises a data analysis module, a controller, a reminding module, a fuel quantity monitoring module, a management module and a behavior monitoring module; the data analysis module is used for receiving the vehicle data and analyzing the vehicle data to obtain a deviation value of the vehicle; the driver can be prompted to adjust the driving route in time according to the deviation value, and the delivery time of goods is prevented from being delayed; the fuel quantity monitoring module is used for monitoring the fuel quantity of the vehicle in real time, and the management module is used for receiving a refueling signal and distributing a corresponding refueling station to refuel; the corresponding gas station can be reasonably recommended to refuel according to the distribution value, so that the refueling efficiency is improved, and the delivery time of goods is prevented from being delayed; the behavior monitoring module is used for monitoring the condition of a driver in the driving process in real time, so that the risk of accidents caused by fatigue driving of the driver is avoided, and the personal safety of the driver is effectively protected.
Description
Technical Field
The invention relates to the field of logistics transportation, in particular to a logistics vehicle-mounted navigation system based on big data.
Background
Currently, as global economy increases, and particularly as electronic commerce develops, with more and more goods being delivered and distributed from one place to another every day, logistics services are playing an increasingly important role in people's social and economic lives.
In real life, the logistics distribution personnel drive the logistics vehicle loaded with goods to safely deliver the goods to the residence of a user. With the development of online shopping, more and more goods are distributed across cities and provinces; logistics distribution personnel often need long-distance driving; the navigation system of the existing logistics vehicle has a single function, when the quantity of the logistics vehicle is insufficient, a corresponding gas station cannot be reasonably recommended to refuel, the logistics vehicle is often stopped on a road to wait for the support of the vehicle passing by, the delivery time of goods can be delayed, and a lot of inconvenience is brought to logistics distribution personnel; meanwhile, the logistics distribution personnel are easy to accumulate fatigue during long-distance driving, the existing navigation system is lack of state monitoring on the logistics distribution personnel, fatigue alarm cannot be carried out on the state of the logistics distribution personnel, and the driving danger of the distribution personnel is improved; and the problems that the logistics vehicles deviate from the route and the delivery time of goods is delayed due to the fact that analysis and early warning cannot be carried out in real time according to the deviation values of the logistics vehicles exist.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a logistics vehicle-mounted navigation system based on big data.
The purpose of the invention can be realized by the following technical scheme: a logistics vehicle-mounted navigation system based on big data comprises a GPS navigation module, a data acquisition module, a data analysis module, a controller, a reminding module, a voice reading module, an oil quantity monitoring module, a management module, a display module and a behavior monitoring module;
the GPS navigation module is used for planning a navigation path after acquiring a starting point and an end point of logistics transportation and sending the navigation path to the display module for data navigation;
the GPS navigation module is in communication connection with the data acquisition module, the data acquisition module is used for acquiring vehicle data during driving and transmitting the vehicle data to the data analysis module, and the vehicle data comprises a driving direction, a driving speed and a real-time position of a vehicle;
the data analysis module is used for receiving the vehicle data and analyzing the vehicle data to obtain a deviation value of the vehicle and sending the deviation value to the controller;
the controller is configured to compare the deviation value to a standard deviation threshold; if the deviation value is greater than the standard deviation threshold value and the time length of the deviation value greater than the standard deviation threshold value exceeds the standard time length threshold value, the controller drives the reminding module to send out a reminding signal, the reminding module is used for sending the reminding signal to the voice reading module, and the voice reading module is used for reading the current route in a voice reading mode when receiving the reminding signal, and please plan again; the oil quantity monitoring module is used for monitoring the oil quantity of the vehicle in real time;
the fuel quantity monitoring module is used for transmitting a refueling signal to the management module, and the management module is used for receiving the refueling signal and distributing a corresponding refueling station to refuel;
the behavior monitoring module is used for recording the condition of a driver in real time in the driving process, acquiring real-time video information of the driver and analyzing the real-time video information;
the behavior monitoring module is used for transmitting the safety flameout instruction to the controller, the controller remotely controls the fuel cut-off of the vehicle, and the safety flameout instruction is intelligently executed according to the speed and the road condition, and the vehicle is parked while being leaned on.
Further, the specific analysis steps of the data analysis module are as follows:
the method comprises the following steps: obtaining the deviation angle of the vehicle and marking G1; the method specifically comprises the following steps:
s11: establishing a plane two-dimensional coordinate system by taking the starting point position of the vehicle as a coordinate origin;
s12: marking three points near the real-time position of the vehicle on the navigation path as (X0, Y0), (X1, Y1) and (X2, Y2); calculating the circular arc track passing through the three points; if the three points are on the same straight line, the circular arc track passing through the three points is the straight line;
s13: obtaining a tangent of the track; calculating the angle difference between the tangential direction and the driving direction of the vehicle to obtain the deviation angle of the vehicle; when the arc track is a straight line, the tangent of the track is the straight line;
step two: acquiring the running speed of the vehicle and marking as G2;
step three: obtaining the offset distance of the vehicle and marking as G3; the method specifically comprises the following steps:
s31: acquiring coordinates of a real-time position of the vehicle and marking the coordinates as (X3, Y3); marking point (X3, Y3) as a verification point;
s32: acquiring a reference point corresponding to the real-time position of the vehicle in the navigation path;
the reference point acquisition criterion is: acquiring a plurality of non-coincident corresponding points in the navigation path with the points (X3, Y3), calculating the distance between the corresponding points and the reference points, and marking the corresponding points with the closest distance as the reference points; marking the coordinates of the reference point as (X4, Y4);
s33: using formulas
Obtaining a deviation distance G3;
step four: normalizing the deviation angle, the driving speed and the deviation distance and taking the numerical values of the deviation angle, the driving speed and the deviation distance; the deviation value G4 of the vehicle is obtained by using the formula G4 ═ G1 × a1+ G2 × a2+ G3 × a3, wherein a1, a2 and a3 are coefficient factors.
Further, the specific monitoring steps of the oil quantity monitoring module are as follows:
p1: acquiring the current oil quantity of the vehicle, and marking the current oil quantity as L1;
p2: comparing the current oil quantity L1 with a standard oil quantity threshold line;
if L1 is less than or equal to the standard oil mass threshold, the state is to be verified;
p3: when the logistics vehicle is in a state to be verified, acquiring the real-time position of the logistics vehicle and the end point position of a planned path; calculating the path length between the real-time position and the end position in the planned path, and marking as the residual path length L2;
p3: acquiring the running speed G2 of the vehicle;
acquiring an oil consumption value for maintaining the current running speed of the vehicle and marking the oil consumption value as H1; obtaining the distance L3 which can be driven by the current fuel quantity of the vehicle by using the formula L3 which is G2 XL 1/H1;
p4: comparing the remaining path length L2 with the distance L3 which can be traveled by the current oil quantity of the vehicle;
if L3 is larger than L2, normally driving;
and if L3 is less than or equal to L2, generating a refueling signal.
Further, the specific allocation steps of the management module are as follows:
v1: the management module receives the refueling signal and marks the moment when the management module receives the refueling signal as reference moment; acquiring the real-time position of the vehicle at the reference moment and marking the real-time position as a reference position;
v2: establishing a plane two-dimensional coordinate system by taking the reference position as a coordinate origin and the current direction of the planned path as a forward ordinate axis;
v3: acquiring a distance L3 that the vehicle can travel by the current fuel quantity, and acquiring a fuel station within the area range of the radius L3 by taking a reference position as an origin; marking coordinates of the gas station as (X 'i, Y' i);
using formulas
Obtaining an inter-station distance R1;
v4: according to the point (X 'i, Y' i), executing steps S31-S33, obtaining the deviation distance of the gas station relative to the planned path, and marking as a station deviation distance R2;
v5: according to the points (X 'i, Y' i), judging the position of the gas station; the method specifically comprises the following steps:
if Y' i is larger than 0, judging that the gas station is positioned in front of the planned path, and enabling SD to be 1;
if Y' i is less than or equal to 0, judging that the gas station is positioned behind the planned path, and making SD equal to 0;
v6: collecting basic data of a gas station, wherein the basic data comprises human resources, the number of waiting vehicles and the remaining fuel stock of the gas station; the human resources refer to the number of service personnel of the gas station;
label the gas station's human resources as F1; marking the number of waiting at the gas station as F2; marking the remaining fuel inventory at the fueling station as F3;
acquiring an addition value F4 of the gas station by using a formula F4 ═ F1 × a4+ F3 × a5-F2 × a6, wherein a4, a5 and a6 are coefficient factors;
v7: obtaining a gasoline station distribution value R3 by using a formula R3-1/R1 × b1+1/R2 × b2+ SD × b3+ F4 × b4, wherein b1, b2, b3 and b4 are coefficient factors;
v8: marking the gas station with the maximum assigned value R3 as a target gas station, and transmitting the position of the target gas station to a controller, wherein the controller is used for transmitting the position of the target gas station to a GPS navigation module; the GPS navigation module provides a navigation line for the vehicle to go to the target gas station according to the position of the target gas station and transmits the navigation line to the display module for real-time display.
Further, the specific analysis steps of the behavior monitoring module are as follows:
VV 1: processing the real-time video information to obtain facial image information of a driver;
VV 2: judging the disappearance time of facial image information;
when the facial image information disappearance time of the driver is greater than a preset time value ET1, judging that the driver is in a vague state, and generating a safety flameout instruction;
VV 3: a section of video is adopted, and each frame in the video is processed; marking an eye region ROI from the facial image information, and judging whether the eyes are open or not; the specific judgment method comprises the following steps:
VV 31: marking the area of the eye region ROI as A;
VV 32: acquiring the exposed area of the eyes of the user at the moment and marking the exposed area as B;
VV 33: dividing the exposed area by the area of the eye region ROI to obtain the eye exposure ratio PC of the user, i.e. PC is B/a;
if the PC is smaller than the preset proportion threshold value and the time that the PC is smaller than the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in a closed state;
if the PC is greater than or equal to the preset proportion threshold value and the time that the PC is greater than or equal to the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in an open state;
VV 4: when the eyes are in the closed state, judging the duration of the closed state of the eyes;
when the duration of the eyes in the closed state is greater than or equal to a preset closing threshold, judging that the user is in a drowsy state, and generating a safety flameout instruction;
when the duration of the eye in the closed state is less than a preset closing threshold, further processing the video information;
VV 5: accumulating the duration of the closed state of the eyes of a driver within t time in the driving process to form total closed duration BH 1; t is a set threshold;
counting the times that the eyes of a driver are in a closed state within t time in the driving process and marking as BH 2;
counting the times of yawning of a driver within t time in the driving process and marking as BH 3;
obtaining the fatigue coefficient BZ of the driver by using a formula BZ (BH 1 xz 1+ BH2 xz 2+ BH3 xz 3); wherein z1, z2 and z3 are all coefficient factors;
VV 6: comparing the fatigue coefficient BZ to a standard fatigue threshold;
and if the BZ is larger than or equal to the standard fatigue threshold value, judging that the driver is in fatigue driving, and generating a safe flameout instruction.
The invention has the beneficial effects that:
1. the data analysis module is used for receiving the vehicle data and analyzing the vehicle data; obtaining the deviation angle of the vehicle and marking G1; acquiring the running speed of the vehicle and marking as G2; obtaining the offset distance of the vehicle and marking as G3; obtaining a deviation value G4 of the vehicle by using a formula G4-G1 × a1+ G2 × a2+ G3 × a3, wherein the controller is used for comparing the deviation value with a standard deviation threshold value; if the deviation value is greater than the standard deviation threshold value and the time length of the deviation value greater than the standard deviation threshold value exceeds the standard time length threshold value, the controller drives the reminding module to send out a reminding signal, the reminding module is used for sending the reminding signal to the voice reading module, and the voice reading module is used for reading the 'current route is deviated and please plan again' when receiving the reminding signal; prompting a driver to adjust a driving route and avoiding delaying the delivery time of goods;
2. the oil mass monitoring module is used for monitoring the oil mass of the vehicle in real time, acquiring the current oil mass of the vehicle, and marking the current oil mass as L1; comparing the current oil quantity L1 with a standard oil quantity threshold line; if L1 is less than or equal to the standard oil mass threshold, the state is to be verified; when the logistics vehicle is in a state to be verified, acquiring the real-time position of the logistics vehicle and the end point position of a planned path; calculating the path length between the real-time position and the end position in the planned path, and marking as the residual path length L2; acquiring the running speed G2 of the vehicle; acquiring an oil consumption value for maintaining the current running speed of the vehicle and marking the oil consumption value as H1; obtaining the distance L3 which can be driven by the current fuel quantity of the vehicle by using the formula L3 which is G2 XL 1/H1; comparing the remaining path length L2 with the distance L3 which can be traveled by the current oil quantity of the vehicle; if L3 is larger than L2, normally driving; if L3 is less than or equal to L2, generating an oiling signal; the management module is used for receiving the refueling signal and distributing a corresponding refueling station to refuel; obtaining the distribution value R3 of the obtained gas station, and marking the gas station with the maximum distribution value R3 as a target gas station; the corresponding gas station can be reasonably recommended to refuel according to the distribution value, so that the refueling efficiency is improved, and the delivery time of goods is prevented from being delayed;
3. the behavior monitoring module is used for recording the condition of a driver in a driving process in real time, acquiring real-time video information of the driver, analyzing the real-time video information, processing the real-time video information and acquiring facial image information of the driver; judging the disappearance time of facial image information; when the facial image information disappearance time of the driver is greater than a preset time value ET1, judging that the driver is in a vague state, and generating a safety flameout instruction; a section of video is adopted, and each frame in the video is processed; marking an eye region ROI from the facial image information, and judging whether the eyes are open or not; when the eyes are in the closed state, judging the duration of the closed state of the eyes; when the duration of the eyes in the closed state is greater than or equal to a preset closing threshold, judging that the user is in a drowsy state, and generating a safety flameout instruction; when the duration of the eye in the closed state is less than a preset closing threshold, further processing the video information to obtain a fatigue coefficient BZ of the driver; if BZ is larger than or equal to the standard fatigue threshold value, judging fatigue driving of the driver, generating a safety flameout instruction, avoiding the risk of accidents of the driver caused by fatigue driving, and effectively protecting the personal safety of the driver.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
FIG. 2 is a block diagram of a system according to embodiment 1 of the present invention;
FIG. 3 is a block diagram of a system according to embodiment 2 of the present invention;
FIG. 4 is a block diagram of a system according to embodiment 3 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-4, a logistics vehicle-mounted navigation system based on big data comprises a GPS navigation module, a data acquisition module, a data analysis module, a controller, a reminding module, a voice reading module, an oil amount monitoring module, a management module, a display module and a behavior monitoring module;
example 1
As shown in fig. 2; the GPS navigation module is used for planning a navigation path after acquiring a starting point and an end point of logistics transportation and sending the navigation path to the display module for data navigation;
the GPS navigation module is in communication connection with the data acquisition module, the data acquisition module is used for acquiring vehicle data during driving and transmitting the vehicle data to the data analysis module, and the vehicle data comprises a driving direction, a driving speed and a real-time position of a vehicle;
the data analysis module is used for receiving the vehicle data and analyzing the vehicle data to obtain a deviation value of the vehicle, and the specific analysis steps are as follows:
the method comprises the following steps: obtaining the deviation angle of the vehicle and marking G1; the method specifically comprises the following steps:
s11: establishing a plane two-dimensional coordinate system by taking the starting point position of the vehicle as a coordinate origin;
s12: marking three points near the real-time position of the vehicle on the navigation path as (X0, Y0), (X1, Y1) and (X2, Y2); calculating the circular arc track passing through the three points; if the three points are on the same straight line, the circular arc track passing through the three points is the straight line;
s13: obtaining a tangent of the track; calculating the angle difference between the tangential direction and the driving direction of the vehicle to obtain the deviation angle of the vehicle; when the arc track is a straight line, the tangent of the track is the straight line;
step two: acquiring the running speed of the vehicle and marking as G2;
step three: obtaining the offset distance of the vehicle and marking as G3; the method specifically comprises the following steps:
s31: acquiring coordinates of a real-time position of the vehicle and marking the coordinates as (X3, Y3); marking point (X3, Y3) as a verification point;
s32: acquiring a reference point corresponding to the real-time position of the vehicle in the navigation path;
the reference point acquisition criterion is: acquiring a plurality of non-coincident corresponding points in the navigation path with the points (X3, Y3), calculating the distance between the corresponding points and the reference points, and marking the corresponding points with the closest distance as the reference points; marking the coordinates of the reference point as (X4, Y4);
s33: using formulas
Obtaining a deviation distance G3;
step four: normalizing the deviation angle, the driving speed and the deviation distance and taking the numerical values of the deviation angle, the driving speed and the deviation distance;
obtaining a deviation value G4 of the vehicle by using a formula G4 which is G1 × a1+ G2 × a2+ G3 × a3, wherein a1, a2 and a3 are coefficient factors, for example, a1 takes a value of 0.3, a2 takes a value of 0.4 and a3 takes a value of 0.6; wherein, the smaller the deviation angle, the smaller the driving speed and the smaller the deviation distance; the smaller the deviation value G4 of the vehicle;
the data analysis module is used for sending the deviation value G4 to the controller, and the controller is used for comparing the deviation value with a standard deviation threshold value; if the deviation value is greater than the standard deviation threshold value and the time length of the deviation value greater than the standard deviation threshold value exceeds the standard time length threshold value, the controller drives the reminding module to send out a reminding signal, the reminding module is used for sending the reminding signal to the voice reading module, and the voice reading module is used for reading the 'current route is deviated and please plan again' when receiving the reminding signal; prompting a driver to adjust a driving route;
example 2
As shown in fig. 3; the oil mass monitoring module is used for carrying out real-time supervision to the oil mass of vehicle, and oil mass monitoring module's specific monitoring step is as follows:
p1: acquiring the current oil quantity of the vehicle, and marking the current oil quantity as L1;
p2: comparing the current oil quantity L1 with a standard oil quantity threshold line;
if L1 is less than or equal to the standard oil mass threshold, the state is to be verified;
p3: when the logistics vehicle is in a state to be verified, acquiring the real-time position of the logistics vehicle and the end point position of a planned path; calculating the path length between the real-time position and the end position in the planned path, and marking as the residual path length L2;
p3: acquiring the running speed G2 of the vehicle;
acquiring an oil consumption value for maintaining the current running speed of the vehicle and marking the oil consumption value as H1; obtaining the distance L3 which can be driven by the current fuel quantity of the vehicle by using the formula L3 which is G2 XL 1/H1;
p4: comparing the remaining path length L2 with the distance L3 which can be traveled by the current oil quantity of the vehicle;
if L3 is larger than L2, normally driving;
if L3 is less than or equal to L2, generating an oiling signal;
the oil quantity monitoring module is used for transmitting the refueling signal to the management module, and the management module is used for receiving the refueling signal and distributing a corresponding refueling station to refuel; the specific distribution steps are as follows:
v1: the management module receives the refueling signal and marks the moment when the management module receives the refueling signal as reference moment; acquiring the real-time position of the vehicle at the reference moment and marking the real-time position as a reference position;
v2: establishing a plane two-dimensional coordinate system by taking the reference position as a coordinate origin and the current direction of the planned path as a forward ordinate axis;
v3: acquiring a distance L3 that the vehicle can travel by the current fuel quantity, and acquiring a fuel station within the area range of the radius L3 by taking a reference position as an origin; marking coordinates of the gas station as (X 'i, Y' i);
using formulas
Obtaining an inter-station distance R1;
v4: according to the point (X 'i, Y' i), executing steps S31-S33, obtaining the deviation distance of the gas station relative to the planned path, and marking as a station deviation distance R2;
v5: according to the points (X 'i, Y' i), judging the position of the gas station; the method specifically comprises the following steps:
if Y' i is larger than 0, judging that the gas station is positioned in front of the planned path, and enabling SD to be 1;
if Y' i is less than or equal to 0, judging that the gas station is positioned behind the planned path, and making SD equal to 0;
v6: collecting basic data of a gas station, wherein the basic data comprises human resources, the number of waiting vehicles and the remaining fuel stock of the gas station; the human resources refer to the number of service personnel of the gas station;
label the gas station's human resources as F1; marking the number of waiting at the gas station as F2; marking the remaining fuel inventory at the fueling station as F3;
acquiring an addition value F4 of the gas station by using a formula F4 which is F1 × a4+ F3 × a5-F2 × a6, wherein a4, a5 and a6 are coefficient factors, for example, a4 takes a value of 0.31, a5 takes a value of 0.55 and a6 takes a value of 0.42;
v7: obtaining a distribution value R3 of the gas station by using a formula R3 which is 1/R1 × b1+1/R2 × b2+ SD × b3+ F4 × b4, wherein b1, b2, b3 and b4 are coefficient factors, for example, b1 takes the value of 0.23, b2 takes the value of 0.48, b3 takes the value of 0.59 and b4 takes the value of 0.71;
v8: marking the gas station with the maximum assigned value R3 as a target gas station, and transmitting the position of the target gas station to a controller, wherein the controller is used for transmitting the position of the target gas station to a GPS navigation module; the GPS navigation module provides a navigation path for the vehicle to go to the target gas station according to the position of the target gas station and transmits the navigation path to the display module for real-time display;
example 3
As shown in fig. 4; the behavior monitoring module is used for recording the condition of a driver in the driving process in real time, acquiring the real-time video information of the driver and analyzing the real-time video information, and the specific analysis steps are as follows:
VV 1: processing the real-time video information to obtain facial image information of a driver;
VV 2: judging the disappearance time of facial image information;
when the facial image information disappearance time of the driver is greater than a preset time value ET1, judging that the driver is in a vague state, and generating a safety flameout instruction;
VV 3: a section of video is adopted, and each frame in the video is processed; marking an eye region ROI from the facial image information, and judging whether the eyes are open or not; the specific judgment method comprises the following steps:
VV 31: marking the area of the eye region ROI as A;
VV 32: acquiring the exposed area of the eyes of the user at the moment and marking the exposed area as B;
VV 33: dividing the exposed area by the area of the eye region ROI to obtain the eye exposure ratio PC of the user, i.e. PC is B/a;
if the PC is smaller than the preset proportion threshold value and the time that the PC is smaller than the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in a closed state;
if the PC is greater than or equal to the preset proportion threshold value and the time that the PC is greater than or equal to the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in an open state;
VV 4: when the eyes are in the closed state, judging the duration of the closed state of the eyes;
when the duration of the eyes in the closed state is greater than or equal to a preset closing threshold, judging that the user is in a drowsy state, and generating a safety flameout instruction;
when the duration of the eye in the closed state is less than a preset closing threshold, further processing the video information;
VV 5: accumulating the duration of the closed state of the eyes of a driver within t time in the driving process to form total closed duration BH 1; t is a set threshold;
counting the times that the eyes of a driver are in a closed state within t time in the driving process and marking as BH 2;
counting the times of yawning of a driver within t time in the driving process and marking as BH 3;
obtaining the fatigue coefficient BZ of the driver by using a formula BZ (BH 1 xz 1+ BH2 xz 2+ BH3 xz 3); wherein z1, z2 and z3 are coefficient factors, for example, z1 takes 0.55, z2 takes 0.46 and z3 takes 0.76;
VV 6: comparing the fatigue coefficient BZ to a standard fatigue threshold;
if BZ is larger than or equal to the standard fatigue threshold value, judging fatigue driving of a driver, and generating a safe flameout instruction;
the behavior monitoring module is used for transmitting the safety flameout instruction to the controller, the controller remotely controls the fuel cut-off of the vehicle, intelligently executes the safety flameout instruction according to the speed and road conditions, and the vehicle stops while keeping; the risk of accidents caused by fatigue driving of drivers is avoided, and the personal safety of the drivers is effectively protected.
The working principle of the invention is as follows:
when the logistics vehicle-mounted navigation system based on big data works, the data acquisition module is used for acquiring vehicle data during driving and transmitting the vehicle data to the data analysis module, and the data analysis module is used for receiving the vehicle data and analyzing the vehicle data; obtaining the deviation angle of the vehicle and marking G1; acquiring the running speed of the vehicle and marking as G2; obtaining the offset distance of the vehicle and marking as G3; obtaining a deviation value G4 of the vehicle by using a formula G4-G1 × a1+ G2 × a2+ G3 × a3, wherein the controller is used for comparing the deviation value with a standard deviation threshold value; if the deviation value is greater than the standard deviation threshold value and the time length of the deviation value greater than the standard deviation threshold value exceeds the standard time length threshold value, the controller drives the reminding module to send out a reminding signal, the reminding module is used for sending the reminding signal to the voice reading module, and the voice reading module is used for reading the 'current route is deviated and please plan again' when receiving the reminding signal; prompting a driver to adjust a driving route;
the oil quantity monitoring module is used for monitoring the oil quantity of the vehicle in real time, acquiring the current oil quantity of the vehicle and marking the current oil quantity as L1; comparing the current oil quantity L1 with a standard oil quantity threshold line; if L1 is less than or equal to the standard oil mass threshold, the state is to be verified; when the logistics vehicle is in a state to be verified, acquiring the real-time position of the logistics vehicle and the end point position of a planned path; calculating the path length between the real-time position and the end position in the planned path, and marking as the residual path length L2; acquiring the running speed G2 of the vehicle; acquiring an oil consumption value for maintaining the current running speed of the vehicle and marking the oil consumption value as H1; obtaining the distance L3 which can be driven by the current fuel quantity of the vehicle by using the formula L3 which is G2 XL 1/H1; comparing the remaining path length L2 with the distance L3 which can be traveled by the current oil quantity of the vehicle; if L3 is larger than L2, normally driving; if L3 is less than or equal to L2, generating an oiling signal; the management module is used for receiving the refueling signal and distributing a corresponding refueling station to refuel; obtaining the distribution value R3 of the obtained gas station, and marking the gas station with the maximum distribution value R3 as a target gas station;
the behavior monitoring module is used for recording the condition of a driver in real time in the driving process, acquiring real-time video information of the driver, analyzing the real-time video information, processing the real-time video information and acquiring facial image information of the driver; judging the disappearance time of facial image information; when the facial image information disappearance time of the driver is greater than a preset time value ET1, judging that the driver is in a vague state, and generating a safety flameout instruction; otherwise, a section of video is adopted, and each frame in the video is processed; marking an eye region ROI from the facial image information, and judging whether the eyes are open or not; when the eyes are in the closed state, judging the duration of the closed state of the eyes; when the duration of the eyes in the closed state is greater than or equal to a preset closing threshold, judging that the user is in a drowsy state, and generating a safety flameout instruction; when the duration of the eye in the closed state is less than a preset closing threshold, further processing the video information to obtain a fatigue coefficient BZ of the driver; and if the BZ is larger than or equal to the standard fatigue threshold value, judging that the driver is in fatigue driving, and generating a safe flameout instruction.
The formula and the coefficient factor are both obtained by acquiring a large amount of data to perform software simulation and performing parameter setting processing by corresponding experts, and the formula and the coefficient factor which are consistent with a real result are obtained.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A logistics vehicle-mounted navigation system based on big data is characterized by comprising a GPS navigation module, a data acquisition module, a data analysis module, a controller, a reminding module, a voice reading module, an oil quantity monitoring module, a management module, a display module and a behavior monitoring module;
the GPS navigation module is used for planning a navigation path after acquiring a starting point and an end point of logistics transportation and sending the navigation path to the display module for data navigation;
the GPS navigation module is in communication connection with the data acquisition module, the data acquisition module is used for acquiring vehicle data during driving and transmitting the vehicle data to the data analysis module, and the vehicle data comprises a driving direction, a driving speed and a real-time position of a vehicle;
the data analysis module is used for receiving the vehicle data and analyzing the vehicle data to obtain a deviation value of the vehicle and sending the deviation value to the controller;
the controller is configured to compare the deviation value to a standard deviation threshold; if the deviation value is greater than the standard deviation threshold value and the time length of the deviation value greater than the standard deviation threshold value exceeds the standard time length threshold value, the controller drives the reminding module to send out a reminding signal, the reminding module is used for sending the reminding signal to the voice reading module, and the voice reading module is used for reading the current route in a voice reading mode when receiving the reminding signal, and please plan again; the oil quantity monitoring module is used for monitoring the oil quantity of the vehicle in real time;
the fuel quantity monitoring module is used for transmitting a refueling signal to the management module, and the management module is used for receiving the refueling signal and distributing a corresponding refueling station to refuel;
the behavior monitoring module is used for recording the condition of a driver in real time in the driving process, acquiring real-time video information of the driver and analyzing the real-time video information;
the behavior monitoring module is used for transmitting the safety flameout instruction to the controller, the controller remotely controls the fuel cut-off of the vehicle, and the safety flameout instruction is intelligently executed according to the speed and the road condition, and the vehicle is parked while being leaned on.
2. The logistics vehicle-mounted navigation system based on big data as claimed in claim 1, wherein the specific analysis steps of the data analysis module are as follows:
the method comprises the following steps: obtaining the deviation angle of the vehicle and marking G1; the method specifically comprises the following steps:
s11: establishing a plane two-dimensional coordinate system by taking the starting point position of the vehicle as a coordinate origin;
s12: marking three points near the real-time position of the vehicle on the navigation path as (X0, Y0), (X1, Y1) and (X2, Y2); calculating the circular arc track passing through the three points; if the three points are on the same straight line, the circular arc track passing through the three points is the straight line;
s13: obtaining a tangent of the track; calculating the angle difference between the tangential direction and the driving direction of the vehicle to obtain the deviation angle of the vehicle; when the arc track is a straight line, the tangent of the track is the straight line;
step two: acquiring the running speed of the vehicle and marking as G2;
step three: obtaining the offset distance of the vehicle and marking as G3; the method specifically comprises the following steps:
s31: acquiring coordinates of a real-time position of the vehicle and marking the coordinates as (X3, Y3); marking point (X3, Y3) as a verification point;
s32: acquiring a reference point corresponding to the real-time position of the vehicle in the navigation path;
the reference point acquisition criterion is: acquiring a plurality of non-coincident corresponding points in the navigation path with the points (X3, Y3), calculating the distance between the corresponding points and the reference points, and marking the corresponding points with the closest distance as the reference points; marking the coordinates of the reference point as (X4, Y4);
s33: using formulas
Obtaining a deviation distance G3;
step four: normalizing the deviation angle, the driving speed and the deviation distance and taking the numerical values of the deviation angle, the driving speed and the deviation distance; the deviation value G4 of the vehicle is obtained by using the formula G4 ═ G1 × a1+ G2 × a2+ G3 × a3, wherein a1, a2 and a3 are coefficient factors.
3. The logistics vehicle-mounted navigation system based on big data as claimed in claim 1, wherein the specific monitoring steps of the oil quantity monitoring module are as follows:
p1: acquiring the current oil quantity of the vehicle, and marking the current oil quantity as L1;
p2: comparing the current oil quantity L1 with a standard oil quantity threshold line;
if L1 is less than or equal to the standard oil mass threshold, the state is to be verified;
p3: when the logistics vehicle is in a state to be verified, acquiring the real-time position of the logistics vehicle and the end point position of a planned path; calculating the path length between the real-time position and the end position in the planned path, and marking as the residual path length L2;
p3: acquiring the running speed G2 of the vehicle;
acquiring an oil consumption value for maintaining the current running speed of the vehicle and marking the oil consumption value as H1; obtaining the distance L3 which can be driven by the current fuel quantity of the vehicle by using the formula L3 which is G2 XL 1/H1;
p4: comparing the remaining path length L2 with the distance L3 which can be traveled by the current oil quantity of the vehicle;
if L3 is larger than L2, normally driving;
and if L3 is less than or equal to L2, generating a refueling signal.
4. The logistics vehicle-mounted navigation system based on big data as claimed in claim 1, wherein the management module is specifically allocated to the following steps:
v1: the management module receives the refueling signal and marks the moment when the management module receives the refueling signal as reference moment; acquiring the real-time position of the vehicle at the reference moment and marking the real-time position as a reference position;
v2: establishing a plane two-dimensional coordinate system by taking the reference position as a coordinate origin and the current direction of the planned path as a forward ordinate axis;
v3: acquiring a distance L3 that the vehicle can travel by the current fuel quantity, and acquiring a fuel station within the area range of the radius L3 by taking a reference position as an origin; marking coordinates of the gas station as (X 'i, Y' i);
using formulas
Obtaining an inter-station distance R1;
v4: according to the point (X 'i, Y' i), executing steps S31-S33, obtaining the deviation distance of the gas station relative to the planned path, and marking as a station deviation distance R2;
v5: according to the points (X 'i, Y' i), judging the position of the gas station; the method specifically comprises the following steps:
if Y' i is larger than 0, judging that the gas station is positioned in front of the planned path, and enabling SD to be 1;
if Y' i is less than or equal to 0, judging that the gas station is positioned behind the planned path, and making SD equal to 0;
v6: collecting basic data of a gas station, wherein the basic data comprises human resources, the number of waiting vehicles and the remaining fuel stock of the gas station; the human resources refer to the number of service personnel of the gas station;
label the gas station's human resources as F1; marking the number of waiting at the gas station as F2; marking the remaining fuel inventory at the fueling station as F3;
acquiring an addition value F4 of the gas station by using a formula F4 ═ F1 × a4+ F3 × a5-F2 × a6, wherein a4, a5 and a6 are coefficient factors;
v7: obtaining a gasoline station distribution value R3 by using a formula R3-1/R1 × b1+1/R2 × b2+ SD × b3+ F4 × b4, wherein b1, b2, b3 and b4 are coefficient factors;
v8: marking the gas station with the maximum assigned value R3 as a target gas station, and transmitting the position of the target gas station to a controller, wherein the controller is used for transmitting the position of the target gas station to a GPS navigation module; the GPS navigation module provides a navigation line for the vehicle to go to the target gas station according to the position of the target gas station and transmits the navigation line to the display module for real-time display.
5. The logistics vehicle-mounted navigation system based on big data as claimed in claim 1, wherein the specific analysis steps of the behavior monitoring module are as follows:
VV 1: processing the real-time video information to obtain facial image information of a driver;
VV 2: judging the disappearance time of facial image information;
when the facial image information disappearance time of the driver is greater than a preset time value ET1, judging that the driver is in a vague state, and generating a safety flameout instruction;
VV 3: a section of video is adopted, and each frame in the video is processed; marking an eye region ROI from the facial image information, and judging whether the eyes are open or not; the specific judgment method comprises the following steps:
VV 31: marking the area of the eye region ROI as A;
VV 32: acquiring the exposed area of the eyes of the user at the moment and marking the exposed area as B;
VV 33: dividing the exposed area by the area of the eye region ROI to obtain the eye exposure ratio PC of the user, i.e. PC is B/a;
if the PC is smaller than the preset proportion threshold value and the time that the PC is smaller than the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in a closed state;
if the PC is greater than or equal to the preset proportion threshold value and the time that the PC is greater than or equal to the preset proportion threshold value exceeds a first threshold value, judging that the eyes are in an open state;
VV 4: when the eyes are in the closed state, judging the duration of the closed state of the eyes;
when the duration of the eyes in the closed state is greater than or equal to a preset closing threshold, judging that the user is in a drowsy state, and generating a safety flameout instruction;
when the duration of the eye in the closed state is less than a preset closing threshold, further processing the video information;
VV 5: accumulating the duration of the closed state of the eyes of a driver within t time in the driving process to form total closed duration BH 1; t is a set threshold;
counting the times that the eyes of a driver are in a closed state within t time in the driving process and marking as BH 2;
counting the times of yawning of a driver within t time in the driving process and marking as BH 3;
obtaining the fatigue coefficient BZ of the driver by using a formula BZ (BH 1 xz 1+ BH2 xz 2+ BH3 xz 3); wherein z1, z2 and z3 are all coefficient factors;
VV 6: comparing the fatigue coefficient BZ to a standard fatigue threshold;
and if the BZ is larger than or equal to the standard fatigue threshold value, judging that the driver is in fatigue driving, and generating a safe flameout instruction.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170704A (en) * | 2021-11-04 | 2022-03-11 | 国网江西省电力有限公司检修分公司 | Digital intelligent vehicle monitoring system |
| CN114360274A (en) * | 2021-12-13 | 2022-04-15 | 珠海格力智能装备有限公司 | Distribution vehicle navigation method, system, computer equipment and storage medium |
| CN115499776A (en) * | 2022-09-14 | 2022-12-20 | 智能网联汽车(山东)协同创新研究院有限公司 | Internet of things automobile state remote control system |
| CN115951732A (en) * | 2022-12-20 | 2023-04-11 | 铭派技术开发有限公司 | Temperature control system of smart radar host |
| CN116295684A (en) * | 2023-03-07 | 2023-06-23 | 智能网联汽车(山东)协同创新研究院有限公司 | Automobile instantaneous oil consumption monitoring system under intelligent networking environment |
| CN116720800A (en) * | 2023-06-14 | 2023-09-08 | 深圳市深泰创建科技有限公司 | Intelligent logistics scheduling system |
| CN116758723A (en) * | 2023-08-10 | 2023-09-15 | 深圳市明心数智科技有限公司 | Vehicle transportation monitoring method, system and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102878998A (en) * | 2011-07-13 | 2013-01-16 | 上海博泰悦臻电子设备制造有限公司 | Vehicle fueling prompting method based on path programming |
| CN106595689A (en) * | 2016-12-28 | 2017-04-26 | 苏州寅初信息科技有限公司 | Gas station navigation target formulating system based on Internet of Vehicles |
| CN107504977A (en) * | 2017-09-26 | 2017-12-22 | 重庆长安汽车股份有限公司 | Group refueling system for prompting and method |
| CN107993409A (en) * | 2017-12-23 | 2018-05-04 | 合肥微商圈信息科技有限公司 | Vehicle-mounted video monitoring system and method for detecting driver |
| CN111845935A (en) * | 2020-07-31 | 2020-10-30 | 安徽泗州拖拉机制造有限公司 | Automatic navigation steering system of unmanned tractor |
| JP6784424B1 (en) * | 2019-08-01 | 2020-11-11 | 肇▲慶▼学院 | Overwork detection warning system and method based on machine vision |
| CN112071061A (en) * | 2020-09-11 | 2020-12-11 | 谢能丹 | Vehicle service system based on cloud computing and data analysis |
-
2021
- 2021-01-25 CN CN202110097301.0A patent/CN112985436A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102878998A (en) * | 2011-07-13 | 2013-01-16 | 上海博泰悦臻电子设备制造有限公司 | Vehicle fueling prompting method based on path programming |
| CN106595689A (en) * | 2016-12-28 | 2017-04-26 | 苏州寅初信息科技有限公司 | Gas station navigation target formulating system based on Internet of Vehicles |
| CN107504977A (en) * | 2017-09-26 | 2017-12-22 | 重庆长安汽车股份有限公司 | Group refueling system for prompting and method |
| CN107993409A (en) * | 2017-12-23 | 2018-05-04 | 合肥微商圈信息科技有限公司 | Vehicle-mounted video monitoring system and method for detecting driver |
| JP6784424B1 (en) * | 2019-08-01 | 2020-11-11 | 肇▲慶▼学院 | Overwork detection warning system and method based on machine vision |
| CN111845935A (en) * | 2020-07-31 | 2020-10-30 | 安徽泗州拖拉机制造有限公司 | Automatic navigation steering system of unmanned tractor |
| CN112071061A (en) * | 2020-09-11 | 2020-12-11 | 谢能丹 | Vehicle service system based on cloud computing and data analysis |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170704A (en) * | 2021-11-04 | 2022-03-11 | 国网江西省电力有限公司检修分公司 | Digital intelligent vehicle monitoring system |
| CN114360274A (en) * | 2021-12-13 | 2022-04-15 | 珠海格力智能装备有限公司 | Distribution vehicle navigation method, system, computer equipment and storage medium |
| CN114360274B (en) * | 2021-12-13 | 2023-04-07 | 珠海格力智能装备有限公司 | Distribution vehicle navigation method, system, computer equipment and storage medium |
| CN115499776A (en) * | 2022-09-14 | 2022-12-20 | 智能网联汽车(山东)协同创新研究院有限公司 | Internet of things automobile state remote control system |
| CN115499776B (en) * | 2022-09-14 | 2024-01-12 | 深圳市智安智能科技有限公司 | Internet of things automobile state remote control system |
| CN115951732A (en) * | 2022-12-20 | 2023-04-11 | 铭派技术开发有限公司 | Temperature control system of smart radar host |
| CN115951732B (en) * | 2022-12-20 | 2023-10-31 | 铭派技术开发有限公司 | Wisdom radar host computer temperature control system |
| CN116295684B (en) * | 2023-03-07 | 2023-11-24 | 智能网联汽车(山东)协同创新研究院有限公司 | Automobile instantaneous oil consumption monitoring system under intelligent networking environment |
| CN116295684A (en) * | 2023-03-07 | 2023-06-23 | 智能网联汽车(山东)协同创新研究院有限公司 | Automobile instantaneous oil consumption monitoring system under intelligent networking environment |
| CN116720800A (en) * | 2023-06-14 | 2023-09-08 | 深圳市深泰创建科技有限公司 | Intelligent logistics scheduling system |
| CN116720800B (en) * | 2023-06-14 | 2024-03-08 | 深圳市深泰创建科技有限公司 | Intelligent logistics scheduling system |
| CN116758723B (en) * | 2023-08-10 | 2023-11-03 | 深圳市明心数智科技有限公司 | Vehicle transportation monitoring method, system and medium |
| CN116758723A (en) * | 2023-08-10 | 2023-09-15 | 深圳市明心数智科技有限公司 | Vehicle transportation monitoring method, system and medium |
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Effective date of registration: 20210810 Address after: 518109 802, Mingzhu business building, No. 862, Meilong Avenue, Songhe community, Longhua street, Longhua District, Shenzhen, Guangdong Province Applicant after: Shenzhen sulfur shirt Industrial Co.,Ltd. Address before: 510000 502, 39 Hebian street, Datian, gaozhen, Baiyun District, Guangzhou City, Guangdong Province Applicant before: He Guixiang |
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