CN116740920B - Fatigue driving risk road section identification and control method - Google Patents
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Abstract
The invention discloses a fatigue driving risk road section identification and management and control method, which comprises the following steps: s1, collecting road network data of a target area, and performing semantic division on a fatigue driving road section; s2, carrying out continuous driving time statistics and fatigue index calculation on the divided road sections; s3, based on the continuous driving time and the fatigue index, the fatigue driving risk road sections are identified in a grading mode and classified management and control are conducted. The method breaks through the limitation that the traditional control method only focuses on low control efficiency and high dependence on vehicle sensing equipment caused by fatigue driving behaviors of the vehicle, can obviously improve the identification control level of the road network overall fatigue driving risk road sections, and reduces related accident risks.
Description
Technical Field
The invention belongs to the technical field of road driving safety supervision, and particularly relates to a fatigue driving risk road section identification and management and control method.
Background
With rapid development and popularization of automobiles in China, fatigue driving seriously threatens traffic safety, and a fatigue driving formation mechanism, a fatigue driving behavior error and a fatigue early warning and control technology and the like are becoming main research directions of traffic safety. The relevant research of foreign countries is earlier than that of domestic countries, and the relevant research of domestic countries is developed in 2003, and the fatigue driving identification and control are both in the field of cross hot spot research such as traffic safety and automobile safety at home and abroad.
The method mainly comprises three core technologies of fatigue driving recognition, early warning and control at home and abroad, the research field and the method of the method are widely related to safety science, physiology, medicine, behavior science, automobile engineering, information science, electronic detection, intelligent control and the like, wherein important breakthroughs are made in the aspects of fatigue recognition principle and technology, but the detection recognition system has certain problems in the aspects of accuracy, reliability, anti-interference performance, miniaturization, engineering and the like, and in addition, the contact type physiological parameter detection inevitably introduces new risks for safe driving; the early warning and control technology is developed without forming corresponding mature technologies such as fatigue grade classification standards, automatic safe driving systems and the like, and the technologies and systems are difficult to meet engineering application requirements.
The prior art mainly focuses on fatigue driving behavior, performs fatigue driving early warning by means of various technologies from the perspective of drivers, lacks global consciousness, and has blindness and time-space limitation. According to the scheme, from a global view, fatigue driving risk behaviors and characteristic road sections are researched and judged by means of track big data, and a new thought and technical frame is provided for fatigue driving risk prevention.
Disclosure of Invention
Aiming at the defects in the prior art, the fatigue driving risk road section identification and control method provided by the invention solves the problems of low control efficiency and high dependence of vehicle sensor equipment caused by only focusing on the fatigue driving behavior of the vehicle.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a fatigue driving risk road section identification and management and control method comprises the following steps:
s1, collecting road network data of a target area, and performing semantic division on a fatigue driving road section;
s2, carrying out continuous driving time statistics and fatigue index calculation on the divided road sections;
and S3, based on the counted continuous driving time and the fatigue index, the risk grade of the fatigue driving risk road section is identified in a grading mode, and classification management and control are carried out.
Further, the step S1 specifically includes:
s11, collecting road network data of a target area, repairing abnormal road network data and determining a road network information graph;
s12, identifying a logic road intersection in the road network information graph;
in the road network information graph, the centroids of all intersection endpoints within 100 meters are identified as logical road intersections;
s13, dividing the road sections between two adjacent logical road intersections into logical branch road sections, and achieving semantic division of the fatigue driving road sections.
Further, the step S13 specifically includes:
s13-1, connecting adjacent two logic road intersections to obtain rays AB;
s13-2, dividing logic branch road sections according to the length of the ray AB, and numbering the road sections;
when the ray AB is smaller than 1 km, taking the ray AB as a logic branch road section, and numbering the road section;
when the ray AB is more than 1 km but less than 2 km, the midpoint of the ray AB is used for cutting and dividing to obtain two sections of logic branch road sections, and the road sections are numbered;
when the ray AB is larger than 2 km, the logical branch road sections are divided sequentially by taking a logical road intersection A as a starting point and taking 1 km as an interval until the distance from the last dividing point C to the logical road intersection B is smaller than 2 km, the middle point of the ray CB is used for cutting and dividing to obtain two corresponding logical branch road sections, and the road sections are numbered sequentially for each divided logical branch road section;
the road section numbering method comprises the following steps:
sequentially numbering the logic branch road sections in the directions A to B of the logic road intersection as 1,2,3, … and s, and sequentially numbering the logic branch road sections in the directions B to A of the logic road intersection as s+1, s+2, s+3, … and s+s; s is the number of divided logical branch road sections, and s is a positive integer.
Further, the step S2 specifically includes:
s21, collecting historical vehicle track data of a target area, and processing and constructing a travel track set T of each vehicle;
s22, carrying out track path matching on track points in the constructed travel track set T and matching branch road sections of the divided fatigue driving road sections;
s23, carrying out continuous driving time statistics on the matched branch road sections, and further calculating fatigue indexes of the matched branch road sections.
Further, the step S21 specifically includes:
s21-1, collecting historical vehicle track data of a target area, and grouping the track data according to vehicle-to-track points;
s21-2, arranging the track point data of each vehicle according to the sampling time sequence, and eliminating the abnormal track point data to obtain a track sequence;
s21-3, identifying trip break points in the track sequence, and constructing a break point set P;
s21-4, identifying travel sections of the vehicles according to the constructed break point set P, and further obtaining travel track sets of the vehicles.
Further, the step S22 specifically includes:
s22-1, traversing travel track sets of all vehicles;
s22-2, acquiring a neighboring connecting line set in a neighboring range of track points in the travel track set;
s22-3, traversing the corresponding adjacent connecting line sets of the track points with the non-empty adjacent connecting line sets, and determining the included angles between the azimuth rays of the track points and the tangent lines of the adjacent connecting lines;
s22-4, matching the road sections according to the included angle:
when an included angle smaller than 45 degrees exists, the adjacent connecting line with the smallest included angle is used as a matching connecting line, and the corresponding virtual branching road section is used as a matching branching road section;
when all the included angles are larger than 45 degrees, if the maximum included angle of the adjacent connecting line is larger than 135 degrees, the reverse road section of the corresponding road section of the connecting line is used as the matched road section.
Further, the step S23 specifically includes:
s23-1, counting the travel times in a set time interval and average continuous driving duration of matched branch road sections in a set time period;
s23-2, calculating fatigue indexes of the road sections of each branch according to the counted travel times and average continuous driving duration;
wherein the fatigue index includes a relative fatigue index and an absolute fatigue index;
wherein, for any period of time, the relative fatigue index RI of any road section is continuously driven for t hours t The method comprises the following steps:
wherein lambda is a preset time period weight, N is travel times of which the continuous driving time is more than t hours, and N is total travel times;
for any period of time, the absolute fatigue index AI of any road section is continuously driven for t hours t The method comprises the following steps:
AI t ＝λ∑ i cnt i ×w i
wherein cnt i The travel sampling times, w, for the continuous driving period i i For cnt i And (5) corresponding weight.
Further, the step S3 specifically includes:
s31, screening branch road sections with travel times greater than 100;
s32, carrying out fatigue risk level identification on the screened branch road sections according to the calculated fatigue indexes;
s33, carrying out fatigue driving classification control on the branch road sections according to the identified fatigue risk level.
Further, the fatigue risk level in the step S32 includes a relative fatigue risk level and an absolute fatigue risk level;
the relative fatigue risk grades of the branch road sections are classified into 1-10 grades according to the relative fatigue indexes of the branch road sections; and the absolute fatigue risk grades of the branch road sections are subjected to discontinuous sequencing according to the absolute fatigue risk indexes of the branch road sections and the 10 grades according to a natural discontinuous method, so that the corresponding risk grades are obtained.
Further, in the step S33, the method for performing fatigue driving classification control includes:
the branch road sections with the relative fatigue risk level and the absolute fatigue risk level smaller than 4 are not managed and controlled;
reminding a relative fatigue risk level or an absolute fatigue risk level of 5-8 by linking with the electronic navigation map;
setting reminding marks for the branch road sections with the relative fatigue risk level or the absolute fatigue risk level of 8-10 at intervals of 1 km, and simultaneously reminding by linking with an electronic navigation map;
and setting reminding marks and linked electronic navigation maps for reminding and setting temporary rest areas on the front 100 branch road sections in the target area with the relative fatigue index being more than or equal to 3 or the absolute fatigue index at the distance of 1 km between the branch road sections.
The beneficial effects of the invention are as follows:
(1) The method realizes the classification, identification and control of the fatigue driving risk road sections based on the extraction of the historical space-time track data features, and comprises the semantic segmentation of the fatigue driving analysis road sections based on the road structure features, the weighted continuous driving time statistics based on the historical track feature analysis, the classification, identification and control of the fatigue driving risk road sections,
(2) The method breaks through the limitation that the traditional control method only focuses on the low control efficiency and high dependence of the vehicle sensing equipment caused by the fatigue driving behavior of the vehicle;
(3) The method can obviously improve the overall fatigue driving risk road section identification management and control level of the road network and reduce the related accident risk.
Drawings
Fig. 1 is a flowchart of a fatigue driving risk road section identification and control method provided by the invention.
Fig. 2 is a schematic diagram before semantic division of a road section provided by the invention.
Fig. 3 is a schematic diagram after semantic division of a road segment according to the present invention.
Fig. 4 is a schematic diagram of eliminating abnormal trace point data provided by the present invention.
Fig. 5 is a schematic diagram of a trace point matching adjacent connection line provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
The embodiment of the invention provides a fatigue driving risk road section identification and management and control method, as shown in fig. 1, comprising the following steps:
s1, collecting road network data of a target area, and performing semantic division on a fatigue driving road section;
s2, carrying out continuous driving time statistics and fatigue index calculation on the divided road sections;
and S3, based on the counted continuous driving time and the fatigue index, the risk grade of the fatigue driving risk road section is identified in a grading mode, and classification management and control are carried out.
In step S1 of the embodiment of the present invention, original geographic information GIS road network data has an irregular bottom data structure, where road link data cannot be directly used as a research statistical analysis object, semantic division is required according to link data features, and the divided road segment logic units are used as research statistical object units. Based on this, step S1 of the embodiment of the present invention specifically includes:
s11, collecting road network data of a target area, repairing abnormal road network data and determining a road network information graph;
s12, identifying a logic road intersection in the road network information graph;
in the road network information graph, the centroids of all intersection endpoints within 100 meters are identified as logical road intersections;
s13, dividing the road sections between two adjacent logical road intersections into logical branch road sections, and achieving semantic division of the fatigue driving road sections.
In step S11 of this embodiment, road network GIS bottom data in the target area is obtained, and abnormal data, such as suspension lines and data logic errors, are repaired manually.
In step S12 of the present embodiment, as shown in fig. 2, two connecting lines in the road network information graph are defined to have a break point distance smaller than 1 meter as an intersecting state, and for three connecting lines and more end points having an intersecting state, the end points are identified as intersecting points, namely GIS connecting line end points a-h in fig. 2; the centroid of all intersection end points within 100 meters is identified as a logical road intersection, i.e., virtual point AB in fig. 2.
In step S13 of the present embodiment, taking fig. 2 as an example, the semantic segment division is performed on the identified connection line between two adjacent logic road intersections a, B, and the division method specifically includes:
s13-1, connecting adjacent two logic road intersections to obtain rays AB;
s13-2, dividing logic branch road sections according to the length of the ray AB, and numbering the road sections;
when the ray AB is smaller than 1 km, taking the ray AB as a logic branch road section, and numbering the road section;
when the ray AB is more than 1 km but less than 2 km, the midpoint of the ray AB is used for cutting and dividing to obtain two sections of logic branch road sections, and the road sections are numbered;
when the ray AB is larger than 2 km, the logical branch road sections are divided sequentially by taking a logical road intersection A as a starting point and taking 1 km as an interval until the distance from the last dividing point C to the logical road intersection B is smaller than 2 km, the middle point of the ray CB is used for cutting and dividing to obtain two corresponding logical branch road sections, and the road sections are numbered sequentially for each divided logical branch road section;
the road section numbering method comprises the following steps:
sequentially numbering the logic branch road sections in the directions A to B of the logic road intersection as 1,2,3, … and s, and sequentially numbering the logic branch road sections in the directions B to A of the logic road intersection as s+1, s+2, s+3, … and s+s; s is the number of divided logical branch road sections, and s is a positive integer.
The divided logical branching sections are shown in fig. 3, specifically:
(1) When the ray AB is smaller than 1 km, corresponding to connecting lines such as cd, dg and gh which are in the same direction as the AB, giving the same branch line section number s, and connecting lines such as fe, eb and ba which are in the opposite direction to the AB, giving another branch line section number s+1;
(2) When the ray AB is more than 1 km but less than 2 km, the AB is perpendicular to the midpoint C of the AB, and intersecting corresponding connecting lines dg and eb intersect point is used as a cutting point to divide a logic division section and number;
(3) When the ray AB is larger than 2 km, starting from A, breaking AB according to 1 km as a unit until the distance of the rest section is smaller than 2 km, breaking at the midpoint, and dividing and numbering corresponding logic branch sections as shown in figure 3.
The step S2 of the embodiment of the invention specifically comprises the following steps:
s21, collecting historical vehicle track data of a target area, and processing and constructing a travel track set T of each vehicle;
s22, carrying out track path matching on track points in the constructed travel track set T and matching branch road sections of the divided fatigue driving road sections;
s23, carrying out continuous driving time statistics on the matched branch road sections, and further calculating fatigue indexes of the matched branch road sections.
The step S21 of this embodiment specifically includes:
s21-1, collecting historical vehicle track data of a target area, and grouping the track data according to vehicle-to-track points;
s21-2, arranging the track point data of each vehicle according to the sampling time sequence, and eliminating the abnormal track point data to obtain a track sequence;
s21-3, identifying trip break points in the track sequence, and constructing a break point set P;
s21-4, identifying travel sections of the vehicles according to the constructed break point set P, and further obtaining travel track sets of the vehicles.
In step S22-1, truck track data is obtained by sampling data sources such as vehicle satellite positioning, international freight supervision platform, network freight platform, etc., wherein the obtained track point data format is shown in table 1;
TABLE 1 track dot field attributes
In step S21-2, the method for eliminating the abnormal track point data comprises the following steps:
as shown in fig. 4, all the track points P (i) between the start track point and the arrival track point are traversed in time sequence of the track point data, and if the distances between P (i) to P (i-1) and P (i+1) exceed the threshold value of 1 km, and the distance D (i-1, i+1) between P (i-1) and P (i+1) is smaller than 1 km, the P (i) is regarded as outlier track point data and rejected.
In step S21-3, the track points with the forward speed of 0 are identified as trip break points if the accumulated stop time exceeds 30 minutes, and then a break point set is obtained.
In step S21-4, track points with the first speed not being 0 after each trip break point are taken as starting points, track points are continuously crossed from the next trip break point to the next trip break point, and the track points are identified as a trip paragraph, so that a trip track set of each vehicle is obtained.
The step S22 of this embodiment specifically includes:
s22-1, traversing travel track sets of all vehicles;
s22-2, acquiring a neighboring connecting line set in a neighboring range of track points in the travel track set;
s22-3, traversing the corresponding adjacent connecting line sets of the track points with the non-empty adjacent connecting line sets, and determining the included angles between the azimuth rays of the track points and the tangent lines of the adjacent connecting lines;
s22-4, matching the road sections according to the included angle:
when an included angle smaller than 45 degrees exists, the adjacent connecting line with the smallest included angle is used as a matching connecting line, and the corresponding virtual branching road section is used as a matching branching road section;
when all the included angles are larger than 45 degrees, if the maximum included angle of the adjacent connecting line is larger than 135 degrees, the reverse road section of the corresponding road section of the connecting line is used as the matched road section.
In step S22-2, adjacent connection lines refer to the lowest connection line unit in the map GIS data, each connection line is formed by connecting a series of road nodes, and has corresponding road attribute, grade, reverse direction (unidirectional or bidirectional), start point number, end point number and the road-dividing section number determined above; for each track point, acquiring a set of adjacent connecting lines in an adjacent range, searching the set of adjacent connecting lines in a range of 30 meters first, further expanding the searching range to 40 meters if the set is an empty set, and then analogizing until 60 meters, outputting the record and performing road network inspection if no adjacent connecting line exists in 60 meters.
In step S22-4, the virtual branch road section refers to a logical road section divided by rays directed to B through the logical road intersection a; in the process of matching the branch road sections, when all angles are larger than 45 degrees and smaller than 135 degrees, the connection line is failed to match, namely the track point is not considered to pass through the connection line.
In step S22-4, the trace point matching connection lines are shown in FIG. 5, wherein β1 and β2 are included angles of 45 degrees or less.
The step S23 of this embodiment specifically includes:
s23-1, counting the travel times in a set time interval and average continuous driving duration of matched branch road sections in a set time period;
when the travel times are counted, one or more track points belonging to the same vehicle are matched with the corresponding travel times +1 of the connecting line to the branch road section in the period; after all the vehicle tracks are traversed, the number of vehicles passing through each period corresponding to each branch road section and the average continuous driving duration can be counted;
s23-2, calculating fatigue indexes of the road sections of each branch according to the counted travel times and average continuous driving duration;
wherein the fatigue index includes a relative fatigue index and an absolute fatigue index;
wherein, for any period of time, the relative fatigue index RI of any road section is continuously driven for t hours t The method comprises the following steps:
wherein lambda is a preset time period weight, N is travel times of which the continuous driving time is more than t hours, and N is total travel times;
for any period of timeAbsolute fatigue index AI for continuous driving of any road section for t hours t The method comprises the following steps:
AI t ＝λ∑ i cnt i ×w i
wherein cnt i The travel sampling times, w, for the continuous driving period i i For cnt i And (5) corresponding weight.
Specifically, for each branch road section, counting the travel times in the interval according to a time interval of 20 minutes, and if the same travel track has a plurality of continuous track points on the same branch road section, counting repeatedly without taking the driving time corresponding to the last track point as the reference; and counting the average continuous driving duration at each time interval in a preset period, wherein the counting result is shown in a table 2;
table 2 continuous driving time statistics
When calculating the relative fatigue index, the weight of period 1 is 0.5, the weight of period 2 is 1.5, the weight of period 3 is 1.0, and the weight of period 4 is 3.0.
In calculating the absolute fatigue index, w i The settings are shown in table 3;
TABLE 3 continuous drive time weights
| Continuous driving period | Weighting of |
| 2 hours 20 minutes | 1 |
| 2 hours 40 minutes | 1.2 |
| 3 hours | 1.4 |
| ... | ... |
| 9 hours 40 minutes | 5.4 |
| For 10 hours | 5.6 |
| For more than 10 hours | 10 |
The step S3 of the embodiment of the invention specifically comprises the following steps:
s31, screening branch road sections with travel times greater than 100;
s32, carrying out fatigue risk level identification on the screened branch road sections according to the calculated fatigue indexes;
s33, carrying out fatigue driving classification control on the branch road sections according to the identified fatigue risk level.
The fatigue risk level in step S32 of the present embodiment includes a relative fatigue risk level and an absolute fatigue risk level;
the relative fatigue risk grades of the branch road sections are classified into 1-10 grades according to the relative fatigue indexes of the branch road sections; and the absolute fatigue risk grades of the branch road sections are subjected to discontinuous sequencing according to the absolute fatigue risk indexes of the branch road sections and the 10 grades according to a natural discontinuous method, so that the corresponding risk grades are obtained.
Specifically, the relative fatigue risk level correspondence is shown in table 4;
table 4 table of relative fatigue risk level correspondence
In step S33 of the present embodiment, the method for performing fatigue driving classification management and control includes:
the branch road sections with the relative fatigue risk level and the absolute fatigue risk level smaller than 4 are not managed and controlled;
reminding a relative fatigue risk level or an absolute fatigue risk level of 5-8 by linking with the electronic navigation map;
setting reminding marks for the branch road sections with the relative fatigue risk level or the absolute fatigue risk level of 8-10 at intervals of 1 km, and simultaneously reminding by linking with an electronic navigation map;
and setting reminding marks and linked electronic navigation maps for reminding and setting temporary rest areas on the front 100 branch road sections in the target area with the relative fatigue index being more than or equal to 3 or the absolute fatigue index at the distance of 1 km between the branch road sections.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (5)
1. The fatigue driving risk road section identification and management and control method is characterized by comprising the following steps of:
s1, collecting road network data of a target area, and performing semantic division on a fatigue driving road section;
s2, carrying out continuous driving time statistics and fatigue index calculation on the divided road sections;
s3, based on the counted continuous driving time and fatigue index, the risk grade of the fatigue driving risk road section is identified in a grading mode, and classification management and control are carried out;
the step S2 specifically comprises the following steps:
s21, collecting historical vehicle track data of a target area, and processing and constructing a travel track set T of each vehicle;
s22, carrying out track path matching on track points in the constructed travel track set T and matching branch road sections of the divided fatigue driving road sections;
s23, carrying out continuous driving time statistics on the matched branch road sections, and further calculating fatigue indexes of the matched branch road sections;
the step S21 specifically includes:
s21-1, collecting historical vehicle track data of a target area, and grouping the track data according to vehicle-to-track points;
s21-2, arranging the track point data of each vehicle according to the sampling time sequence, and eliminating the abnormal track point data to obtain a track sequence;
s21-3, identifying trip break points in the track sequence, and constructing a break point set P;
s21-4, identifying travel sections of vehicles according to the constructed break point set P, and further obtaining travel track sets of all vehicles;
the step S23 specifically includes:
s23-1, counting the travel times in a set time interval and average continuous driving duration of matched branch road sections in a set time period;
s23-2, calculating fatigue indexes of the road sections of each branch according to the counted travel times and average continuous driving duration;
wherein the fatigue index includes a relative fatigue index and an absolute fatigue index;
wherein, for any period of time, any road section is continuously driventRelative fatigue index in hoursThe method comprises the following steps:
in the method, in the process of the invention,for a preset time period weight, +.>For continuous driving time longer thantTravel times in hours>The total travel times;
for any period of time, any road section is continuously driventAbsolute fatigue index for hoursThe method comprises the following steps:
in the method, in the process of the invention,for continuous driving periodsiTravel sampling times, & gt>Is->Corresponding weights;
the step S3 specifically comprises the following steps:
s31, screening branch road sections with travel times greater than 100;
s32, carrying out fatigue risk level identification on the screened branch road sections according to the calculated fatigue indexes;
s33, carrying out fatigue driving classification control on the branch road sections according to the identified fatigue risk level;
the fatigue risk level in the step S32 includes a relative fatigue risk level and an absolute fatigue risk level;
the relative fatigue risk grades of the branch road sections are classified into 1-10 risk grades according to the relative fatigue indexes of the branch road sections; and the absolute fatigue risk grades of the branch road sections are subjected to discontinuous sequencing according to the absolute fatigue risk indexes of the branch road sections and the 10 grades according to a natural discontinuous method, so that the corresponding risk grades are obtained.
2. The method for identifying and controlling the fatigue driving risk road section according to claim 1, wherein the step S1 is specifically:
s11, collecting road network data of a target area, repairing abnormal road network data and determining a road network information graph;
s12, identifying a logic road intersection in the road network information graph;
in the road network information graph, the centroids of all intersection endpoints within 100 meters are identified as logical road intersections;
s13, dividing the road sections between two adjacent logical road intersections into logical branch road sections, and achieving semantic division of the fatigue driving road sections.
3. The method for identifying and controlling the fatigue driving risk section according to claim 2, wherein the step S13 is specifically:
s13-1, connecting adjacent two logic road intersections to obtain rays AB;
s13-2, dividing logic branch road sections according to the length of the ray AB, and numbering the road sections;
when the ray AB is smaller than 1 km, taking the ray AB as a logic branch road section, and numbering the road section;
when the ray AB is more than 1 km but less than 2 km, the midpoint of the ray AB is used for cutting and dividing to obtain two sections of logic branch road sections, and the road sections are numbered;
when the ray AB is larger than 2 km, the logical branch road sections are divided sequentially by taking a logical road intersection A as a starting point and taking 1 km as an interval until the distance from the last dividing point C to the logical road intersection B is smaller than 2 km, the middle point of the ray CB is used for cutting and dividing to obtain two corresponding logical branch road sections, and the road sections are numbered sequentially for each divided logical branch road section;
the road section numbering method comprises the following steps:
sequentially numbering the logic branch road sections in the directions A to B of the logic road intersection as 1,2,3, … and s, and sequentially numbering the logic branch road sections in the directions B to A of the logic road intersection as s+1, s+2, s+3, … and s+s; s is the number of divided logical branch road sections, and s is a positive integer.
4. The method for identifying and controlling a fatigue driving risk section according to claim 1, wherein the step S22 is specifically:
s22-1, traversing travel track sets of all vehicles;
s22-2, acquiring a neighboring connecting line set in a neighboring range of track points in the travel track set;
s22-3, traversing the corresponding adjacent connecting line sets of the track points with the non-empty adjacent connecting line sets, and determining the included angles between the azimuth rays of the track points and the tangent lines of the adjacent connecting lines;
s22-4, matching the road sections according to the included angle:
when an included angle smaller than 45 degrees exists, the adjacent connecting line with the smallest included angle is used as a matching connecting line, and the corresponding virtual branching road section is used as a matching branching road section;
when all the included angles are larger than 45 degrees, if the maximum included angle of the adjacent connecting line is larger than 135 degrees, the reverse road section of the corresponding road section of the connecting line is used as the matched road section.
5. The method for identifying and controlling the fatigue driving risk road section according to claim 1, wherein in the step S33, the method for performing the fatigue driving classification control is as follows:
the branch road sections with the relative fatigue risk level and the absolute fatigue risk level smaller than 4 are not managed and controlled;
reminding a relative fatigue risk level or an absolute fatigue risk level of 5-8 on a branch road section in linkage with an electronic navigation map;
setting reminding marks for the branch road sections with the relative fatigue risk level or the absolute fatigue risk level of 8-10 at intervals of 1 km, and simultaneously reminding by linking with an electronic navigation map;
and setting reminding marks and linked electronic navigation maps for reminding and setting temporary rest areas on the front 100 branch road sections in the target area with the relative fatigue index being more than or equal to 3 or the absolute fatigue index at the distance of 1 km between the branch road sections.
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