CN117455121B - Information management method and system for intelligent road - Google Patents
Information management method and system for intelligent road Download PDFInfo
- Publication number
- CN117455121B CN117455121B CN202311747884.2A CN202311747884A CN117455121B CN 117455121 B CN117455121 B CN 117455121B CN 202311747884 A CN202311747884 A CN 202311747884A CN 117455121 B CN117455121 B CN 117455121B
- Authority
- CN
- China
- Prior art keywords
- road
- data
- operation unit
- traffic model
- shaking
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 18
- 230000008859 change Effects 0.000 claims description 105
- 230000002159 abnormal effect Effects 0.000 claims description 76
- 238000010586 diagram Methods 0.000 claims description 49
- 238000012512 characterization method Methods 0.000 claims description 38
- 238000001514 detection method Methods 0.000 claims description 25
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Chemical & Material Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Strategic Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Economics (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Game Theory and Decision Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of intelligent roads and discloses an information management method and system of an intelligent road.
Description
Technical Field
The invention relates to the technical field of intelligent roads, in particular to an information management method and system of an intelligent road.
Background
The intelligent road is a road system for intelligently modifying and managing urban road traffic by utilizing advanced information and communication technology. The construction of wisdom road aims at improving traffic operating efficiency, promotes traffic safety, improves environmental quality to provide more convenient, comfortable trip experience for urban resident.
With the rapid development of society, vehicles in cities are increased, but road safety is becoming a concern. Among them, the flatness of the road has a serious influence on the running of the vehicle, and particularly, when the automobile runs on an uneven road during the night, the driver is greatly bothered, and even a traffic accident occurs.
At present, most small urban roads have uneven phenomena, so that the phenomenon of jolt and the like of vehicles in the running process can be caused, and the chassis, the bumpers and the like of the vehicles can be damaged when serious, so that a great deal of time and money are wasted.
Disclosure of Invention
The invention aims to provide an information management method and system for an intelligent road, and aims to solve the problem that the road condition cannot be monitored in real time in the prior art.
The present invention is achieved in that, in a first aspect, the present invention provides an information management method for an intelligent road, including:
acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
generating a road operation characteristic distribution map of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution map of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution map;
and judging the road condition of each specific position of the specified road by analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
Preferably, the step of generating the road operation feature distribution map of each of the road operation units according to the operation data of each of the road operation units on the road traffic model includes:
continuously collecting specific position information of the road running unit on the road traffic model, and associating the specific position information of the road running unit according to a time sequence to obtain a movement path data level of the road running unit;
extracting speed change features and azimuth change features of the road running unit based on the movement path data level to obtain a speed change data level and an azimuth change data level of the road running unit;
continuously collecting the shaking information of the road operation unit on the road traffic model, and connecting the shaking information of the road operation unit according to a time sequence to obtain a movement shaking data level of the road operation unit;
extracting and linking data at the same moment in a speed change data level, an azimuth change data level and a movement shaking change level of the road operation unit to obtain a shaking representation data level of the road operation unit; the motion path data level, the speed change data level, the azimuth change data level, the motion shake change level and the shake representation data level of the road operation unit are all subordinate to the road operation characteristic distribution diagram of the road operation unit;
repeating the steps for each road operation unit to obtain a road operation characteristic distribution diagram of each road operation unit.
Preferably, the step of acquiring the operation influence characteristic distribution map of the road traffic model on each of the road operation units by analyzing each of the road operation characteristic distribution maps includes:
respectively extracting abnormal change characteristics from speed change data levels and azimuth change data levels in a road operation characteristic distribution diagram of each road operation unit according to preset standards, and carrying out first-class abnormal marking on corresponding positions of the road traffic model according to the abnormal change characteristics;
extracting abnormal data features according to a preset standard from shaking representation data levels in a road operation feature distribution diagram of each road operation unit, and carrying out second-type abnormal marks on corresponding positions on the road traffic model according to the abnormal data features;
counting the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model, and generating operation influence features at each position on the road traffic model according to the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain an operation influence feature distribution diagram of the road operation unit.
Preferably, the step of extracting the abnormal data feature of the shake representation data level in the road operation feature distribution map of each road operation unit according to the preset standard includes:
extracting shake representation data of the road operation unit at each moment from a shake representation data level in a road operation characteristic distribution diagram of the road operation unit; the shaking representation data are speed change characteristics, azimuth change characteristics and shaking information of the road running unit at the same moment;
constructing a three-dimensional coordinate system formed by mutually perpendicular X-axis, Y-axis and Z-axis, respectively determining X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the characterization coordinate according to speed change characteristics, azimuth change characteristics and shaking information in the shaking characterization data, and connecting the characterization coordinate with a base point of the three-dimensional coordinate system to form a characterization line segment for describing the shaking characterization data;
calculating the difference value of the characterization line segment according to a theoretical line segment corresponding to the same X-axis coordinate and Y-axis coordinate in a preset database, and judging the result of the difference value calculation according to a preset standard to obtain a first abnormal parameter of the shaking characterization data;
summarizing the characteristic line segments of other shaking characteristic data with X-axis coordinates and Y-axis coordinates meeting a preset standard range into a reference line segment combination, calculating the average deviation degree of the characteristic line segments through the reference line segment combination, and judging the calculated result according to a preset standard to obtain a second abnormal parameter of the shaking characteristic data;
and determining whether the shaking characterization data is an abnormal data characteristic according to the first abnormal parameter and the second abnormal parameter.
In a second aspect, the present invention provides an information management system for an intelligent road, comprising:
the road model construction module is used for acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
the operation data acquisition module is used for generating a road operation characteristic distribution diagram of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution diagram of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution diagram;
and the road condition judging module is used for judging the road condition of each specific position of the specified road through analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
The invention provides an information management method of an intelligent road, which has the following beneficial effects:
according to the invention, a road traffic model is constructed according to the specified road, so that the vehicle is provided with the intelligent terminal carrying the specified software, when the vehicle runs on the specified road, the running data can be acquired, the road running characteristic distribution map is generated according to the running data, the running influence characteristic distribution map of the road traffic model is obtained by analysis, the road condition of each specific position of the specified road is judged by dividing the running influence characteristic distribution map, and the problem that the road condition cannot be monitored in real time in the prior art is solved.
Drawings
Fig. 1 is a schematic diagram of steps of an information management method for an intelligent road according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an information management system for intelligent roads according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The same or similar reference numerals in the drawings of the present embodiment correspond to the same or similar components; in the description of the present invention, it should be understood that, if there is an azimuth or positional relationship indicated by terms such as "upper", "lower", "left", "right", etc., based on the azimuth or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be constructed and operated in a specific azimuth, and thus terms describing the positional relationship in the drawings are merely illustrative and should not be construed as limiting the present invention, and specific meanings of the terms described above may be understood by those of ordinary skill in the art according to specific circumstances.
The implementation of the present invention will be described in detail below with reference to specific embodiments.
Referring to fig. 1 and 2, a preferred embodiment of the present invention is provided.
In a first aspect, the present invention provides an information management method for an intelligent road, including:
s1: acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
s2: generating a road operation characteristic distribution map of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution map of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution map;
s3: and judging the road condition of each specific position of the specified road by analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
Specifically, the shape data of the specified road is collected, the shape data of the specified road includes the road data such as the length, the width, the number of lanes and the like of the specified road, and the road traffic model for feeding back the specified road can be constructed according to the shape data of the specified road.
It should be noted that, when the specified road is a target road to be detected and multiple roads need to be monitored, the multiple roads may be respectively specified as the specified road, and road traffic models are respectively constructed for subsequent monitoring.
More specifically, a vehicle provided with an intelligent terminal carrying prescribed software is designated as a detection vehicle, the intelligent terminal is connected with the communication of the detection vehicle and acquires operation data of the detection vehicle based on the prescribed software, when the operation data of the vehicle shows that the detection vehicle is operated on a prescribed road, namely, a road operation unit is arranged on a road traffic model according to the operation data of the detection vehicle, the operation data of the road operation unit is derived from the intelligent terminal carrying the prescribed software arranged on the detection vehicle,
more specifically, road operation feature distribution diagrams of the road operation units are generated according to operation data of the road operation units on the road traffic model, and operation influence feature distribution diagrams of the road traffic model on the road operation units are obtained through analysis of the road operation feature distribution diagrams.
It should be noted that, the road running feature distribution diagram of the road running unit is a set and description of running features of the road running unit on the road traffic model, that is, the running data of the road running unit on the road traffic model is used to feed back the state information of each position on the road traffic model, and the running influence feature distribution diagram of the road traffic model is used to integrate the state information fed back by each road running unit so as to more accurately describe the road traffic model, so that the road condition of each specific position of the specified road can be judged by analyzing the running influence feature distribution diagram of each road running unit by the road traffic model.
The invention provides an information management method of an intelligent road, which has the following beneficial effects:
according to the invention, a road traffic model is constructed according to the specified road, so that the vehicle is provided with the intelligent terminal carrying the specified software, when the vehicle runs on the specified road, the running data can be acquired, the road running characteristic distribution map is generated according to the running data, the running influence characteristic distribution map of the road traffic model is obtained by analysis, the road condition of each specific position of the specified road is judged by dividing the running influence characteristic distribution map, and the problem that the road condition cannot be monitored in real time in the prior art is solved.
Preferably, the step of generating the road operation feature distribution map of each of the road operation units according to the operation data of each of the road operation units on the road traffic model includes:
s21: continuously collecting specific position information of the road running unit on the road traffic model, and associating the specific position information of the road running unit according to a time sequence to obtain a movement path data level of the road running unit;
s22: extracting speed change features and azimuth change features of the road running unit based on the movement path data level to obtain a speed change data level and an azimuth change data level of the road running unit;
s23: continuously collecting the shaking information of the road operation unit on the road traffic model, and connecting the shaking information of the road operation unit according to a time sequence to obtain a movement shaking data level of the road operation unit;
s24: extracting and linking data at the same moment in a speed change data level, an azimuth change data level and a movement shaking change level of the road operation unit to obtain a shaking representation data level of the road operation unit; the motion path data level, the speed change data level, the azimuth change data level, the motion shake change level and the shake representation data level of the road operation unit are all subordinate to the road operation characteristic distribution diagram of the road operation unit;
s25: repeating the steps for each road operation unit to obtain a road operation characteristic distribution diagram of each road operation unit.
In particular, in general, the width of the road traffic model is greater than the width of the road-running units, that is to say the road-running units do not completely cover the entire area of the road traffic model when running on the road traffic model, so that the specific positions of the coverage of the road-running units on the road traffic model differ from one another.
More specifically, specific position information of the road running unit on the road traffic model is continuously collected, and the specific position information of the road running unit is linked according to a time sequence, so that a running path data level of the road running unit is obtained, the level belongs to a road running characteristic distribution diagram, and the running path data level is one of running characteristics of the road running unit.
More specifically, on the basis of the movement path data level, extracting speed change characteristics and azimuth change characteristics of the road running unit to obtain a speed change data level and an azimuth change data level of the road running unit; the speed change feature is a feature of speed change in the process of running the road running unit on the road traffic model, and the azimuth change feature is a feature of azimuth change in the process of running the road running unit on the road traffic model.
It will be appreciated that the width of the road traffic model is greater than the width of the road traffic units and that most of the road traffic has multiple lanes for the road traffic units to travel, so that the change in orientation includes two aspects, the first being the choice of lanes for the road traffic units on the road traffic model and the second being the specific location of the road traffic units relative to the width of the lanes when they travel on the lanes.
More specifically, the shaking information of the road running unit on the road traffic model is continuously collected, and the shaking information of the road running unit is linked according to the time sequence, so that a movement shaking data level of the road running unit is obtained.
More specifically, extracting and linking data at the same moment in a speed change data level, an azimuth change data level and a movement shake change level of the road operation unit to obtain a shake representation data level of the road operation unit; the movement path data level, the speed change data level, the azimuth change data level, the movement shaking change level and the shaking representation data level of the road operation unit are all subordinate to the road operation characteristic distribution diagram of the road operation unit.
More specifically, the shake characterization data in the shake characterization data hierarchy is used to describe the links among the speed change feature, the azimuth change feature and the movement shake change feature of the road running unit, and it is understood that the links among the three can feed back the road condition: when the movement shaking change feature, the speed change feature and the azimuth change feature of the road movement unit are kept within a certain range, the road condition is normal, and if the movement shaking change feature, the speed change feature and the azimuth change feature exceed a preset range, the road condition is abnormal.
More specifically, the above steps are repeated for each road-running unit to obtain a road-running characteristic distribution map for each road-running unit.
Preferably, the step of acquiring the operation influence characteristic distribution map of the road traffic model on each of the road operation units by analyzing each of the road operation characteristic distribution maps includes:
s31: respectively extracting abnormal change characteristics from speed change data levels and azimuth change data levels in a road operation characteristic distribution diagram of each road operation unit according to preset standards, and carrying out first-class abnormal marking on corresponding positions of the road traffic model according to the abnormal change characteristics;
s32: extracting abnormal data features according to a preset standard from shaking representation data levels in a road operation feature distribution diagram of each road operation unit, and carrying out second-type abnormal marks on corresponding positions on the road traffic model according to the abnormal data features;
s33: counting the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model, and generating operation influence features at each position on the road traffic model according to the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain an operation influence feature distribution diagram of the road operation unit.
In particular, the operation influence feature distribution map of the road operation unit has a plurality of data levels, and the data levels describe the operation of the road operation unit on the road traffic model from different dimensions, so that the operation influence feature distribution map can be subjected to feature extraction from different dimensions, and the road conditions bringing the traffic model can be fed back at a certain angle respectively.
More specifically, the speed change data level and the azimuth change data level in the road operation feature distribution diagram of each road operation unit are respectively extracted according to preset standards, when the speed change data level and the azimuth change data level of the road operation unit show abnormal changes, an abnormal phenomenon exists in the position corresponding to the position on the road traffic model, and a first type of abnormal marking is carried out on the position corresponding to the road traffic model according to the abnormal change features.
More specifically, the extraction of abnormal data features is performed on the shaking representation data levels in the road operation feature distribution diagram of each road operation unit according to a preset standard, when the movement shaking change features, the speed change features and the azimuth change features of the road operation units are kept within a certain range, the road condition is normal, if the movement shaking change features, the speed change features and the azimuth change features exceed a preset range, the road condition is abnormal, and a second type of abnormal mark is performed on the corresponding position of the road traffic model according to the abnormal data features.
More specifically, statistics is performed on the first type of abnormal marks and the second type of abnormal marks at each position on the road traffic model to obtain the occurrence frequency and the occurrence frequency of the first type of abnormal marks and the second type of abnormal marks at each position on the road traffic model, and operation influence features at each position on the road traffic model are generated according to the occurrence frequency and the occurrence frequency of the first type of abnormal marks and the second type of abnormal marks at each position on the road traffic model to obtain an operation influence feature distribution diagram of the road operation unit.
It can be understood that the first type of anomaly markers and the second type of anomaly markers feed back anomaly data occurring at corresponding positions in the road traffic model, and when the anomaly data occurring at a certain position of the road traffic model meets a predetermined standard, the anomaly data does not originate from a problem of detecting vehicles, but rather the anomaly data is influenced by the road traffic model.
Preferably, the step of extracting the abnormal data feature of the shake representation data level in the road operation feature distribution map of each road operation unit according to the preset standard includes:
s321: extracting shake representation data of the road operation unit at each moment from a shake representation data level in a road operation characteristic distribution diagram of the road operation unit; the shaking representation data are speed change characteristics, azimuth change characteristics and shaking information of the road running unit at the same moment;
s322: constructing a three-dimensional coordinate system formed by mutually perpendicular X-axis, Y-axis and Z-axis, respectively determining X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the characterization coordinate according to speed change characteristics, azimuth change characteristics and shaking information in the shaking characterization data, and connecting the characterization coordinate with a base point of the three-dimensional coordinate system to form a characterization line segment for describing the shaking characterization data;
s323: calculating the difference value of the characterization line segment according to a theoretical line segment corresponding to the same X-axis coordinate and Y-axis coordinate in a preset database, and judging the result of the difference value calculation according to a preset standard to obtain a first abnormal parameter of the shaking characterization data;
s324: summarizing the characteristic line segments of other shaking characteristic data with X-axis coordinates and Y-axis coordinates meeting a preset standard range into a reference line segment combination, calculating the average deviation degree of the characteristic line segments through the reference line segment combination, and judging the calculated result according to a preset standard to obtain a second abnormal parameter of the shaking characteristic data;
s325: and determining whether the shaking characterization data is an abnormal data characteristic according to the first abnormal parameter and the second abnormal parameter.
Specifically, shake characterization data of the road running unit at each moment are extracted from shake characterization data levels in a road running characteristic distribution diagram of the road running unit, wherein the shake characterization data comprise data of three data levels at the same moment: speed change characteristics, azimuth change characteristics, and sway information.
More specifically, a three-dimensional coordinate system formed by mutually perpendicular X-axis, Y-axis and Z-axis is constructed, the X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the characterization coordinate are respectively determined according to the speed change characteristic, the azimuth change characteristic and the shaking information in the shaking characterization data, the position information of the characterization coordinate on the three-dimensional coordinate system is determined according to the three data of the shaking characterization data, and the characterization coordinate is connected with the base point of the three-dimensional coordinate system to form a characterization line segment for describing the shaking characterization data.
More specifically, in a preset database, theoretical line segments corresponding to the Z-axis coordinates of each of the X-axis coordinates and the Y-axis coordinates are stored, and these theoretical line segments are used for describing the relationship between the shake information of the vehicle and the speed change feature and the azimuth change feature thereof under the condition that the road condition is normal, and the first abnormal parameter of the shake characterization data is obtained by performing difference calculation on the representative line segments and the theoretical line segments and judging the result of the difference calculation according to a preset standard.
More specifically, the characteristic line segments of other shaking characteristic data with the X-axis coordinate and the Y-axis coordinate conforming to the preset standard range are generalized to be a reference line segment combination, that is, all shaking characteristic data are displayed in a three-dimensional coordinate system at the same time, when abnormality judgment is to be carried out on a certain characteristic line segment, the characteristic line segments with the X-axis coordinate and the Y-axis coordinate in the corresponding ranges in the three-dimensional coordinate system are generalized to be the reference line segment combination according to the X-axis coordinate and the Y-axis coordinate of the characteristic line segment, the average deviation degree of the characteristic line segments is calculated through the reference line segment combination, and the calculated result is judged according to the preset standard, so that a second abnormal parameter of the shaking characteristic data is obtained.
It can be understood that the first abnormal parameter describes a gap between the shake representation data and the preset theoretical data, the second abnormal parameter describes an average gap between the shake representation data and all shake representation data, whether the shake representation data is abnormal is judged through the first abnormal parameter, and whether the abnormality is brought by the detection vehicle is judged through the second abnormal parameter.
Referring to fig. 2, in a second aspect, the present invention provides an information management system of an intelligent road, comprising:
the road model construction module is used for acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
the operation data acquisition module is used for generating a road operation characteristic distribution diagram of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution diagram of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution diagram;
and the road condition judging module is used for judging the road condition of each specific position of the specified road through analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
Each of the above modules operates according to the information management method for an intelligent road provided in the first aspect, and the functions thereof will not be described herein.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (4)
1. An information management method of an intelligent road, comprising:
acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
generating a road operation characteristic distribution map of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution map of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution map; the method comprises the following steps: continuously collecting specific position information of the road running unit on the road traffic model, and associating the specific position information of the road running unit according to a time sequence to obtain a movement path data level of the road running unit; extracting speed change features and azimuth change features of the road running unit based on the movement path data level to obtain a speed change data level and an azimuth change data level of the road running unit; continuously collecting the shaking information of the road operation unit on the road traffic model, and connecting the shaking information of the road operation unit according to a time sequence to obtain a movement shaking data level of the road operation unit; extracting and linking data at the same moment in a speed change data level, an azimuth change data level and a movement shaking change level of the road operation unit to obtain a shaking representation data level of the road operation unit; the motion path data level, the speed change data level, the azimuth change data level, the motion shake change level and the shake representation data level of the road operation unit are all subordinate to the road operation characteristic distribution diagram of the road operation unit; repeating the steps for each road operation unit to obtain a road operation characteristic distribution diagram of each road operation unit;
and judging the road condition of each specific position of the specified road by analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
2. The information management method of an intelligent road according to claim 1, wherein the step of acquiring the operation influence characteristic profile of the road traffic model on each of the road operation units by analyzing each of the road operation characteristic profiles comprises:
respectively extracting abnormal change characteristics from speed change data levels and azimuth change data levels in a road operation characteristic distribution diagram of each road operation unit according to preset standards, and carrying out first-class abnormal marking on corresponding positions of the road traffic model according to the abnormal change characteristics;
extracting abnormal data features according to a preset standard from shaking representation data levels in a road operation feature distribution diagram of each road operation unit, and carrying out second-type abnormal marks on corresponding positions on the road traffic model according to the abnormal data features;
counting the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model, and generating operation influence features at each position on the road traffic model according to the occurrence frequency and the occurrence frequency of the first type abnormal marks and the second type abnormal marks at each position on the road traffic model to obtain an operation influence feature distribution diagram of the road operation unit.
3. The method for information management of intelligent roads according to claim 2, wherein the step of extracting abnormal data features from the sloshing characterization data level in the road operation feature distribution map of each of the road operation units according to the preset criteria comprises:
extracting shake representation data of the road operation unit at each moment from a shake representation data level in a road operation characteristic distribution diagram of the road operation unit; the shaking representation data are speed change characteristics, azimuth change characteristics and shaking information of the road running unit at the same moment;
constructing a three-dimensional coordinate system formed by mutually perpendicular X-axis, Y-axis and Z-axis, respectively determining X-axis coordinate, Y-axis coordinate and Z-axis coordinate of the characterization coordinate according to speed change characteristics, azimuth change characteristics and shaking information in the shaking characterization data, and connecting the characterization coordinate with a base point of the three-dimensional coordinate system to form a characterization line segment for describing the shaking characterization data;
calculating the difference value of the characterization line segment according to a theoretical line segment corresponding to the same X-axis coordinate and Y-axis coordinate in a preset database, and judging the result of the difference value calculation according to a preset standard to obtain a first abnormal parameter of the shaking characterization data;
summarizing the characteristic line segments of other shaking characteristic data with X-axis coordinates and Y-axis coordinates meeting a preset standard range into a reference line segment combination, calculating the average deviation degree of the characteristic line segments through the reference line segment combination, and judging the calculated result according to a preset standard to obtain a second abnormal parameter of the shaking characteristic data;
and determining whether the shaking characterization data is an abnormal data characteristic according to the first abnormal parameter and the second abnormal parameter.
4. An information management system of an intelligent road, comprising:
the road model construction module is used for acquiring shape data of a specified road, constructing a road traffic model corresponding to the specified road based on the shape data, and constructing a road running unit on the road traffic model according to a detection vehicle running on the specified road; the intelligent terminal is connected with the detection vehicle in a communication way and acquires the operation data of the detection vehicle based on the appointed software;
the operation data acquisition module is used for generating a road operation characteristic distribution diagram of each road operation unit according to the operation data of each road operation unit on the road traffic model, and acquiring an operation influence characteristic distribution diagram of each road operation unit by the road traffic model through analysis of each road operation characteristic distribution diagram; the method comprises the following steps: continuously collecting specific position information of the road running unit on the road traffic model, and associating the specific position information of the road running unit according to a time sequence to obtain a movement path data level of the road running unit; extracting speed change features and azimuth change features of the road running unit based on the movement path data level to obtain a speed change data level and an azimuth change data level of the road running unit; continuously collecting the shaking information of the road operation unit on the road traffic model, and connecting the shaking information of the road operation unit according to a time sequence to obtain a movement shaking data level of the road operation unit; extracting and linking data at the same moment in a speed change data level, an azimuth change data level and a movement shaking change level of the road operation unit to obtain a shaking representation data level of the road operation unit; the motion path data level, the speed change data level, the azimuth change data level, the motion shake change level and the shake representation data level of the road operation unit are all subordinate to the road operation characteristic distribution diagram of the road operation unit; repeating the steps for each road operation unit to obtain a road operation characteristic distribution diagram of each road operation unit;
and the road condition judging module is used for judging the road condition of each specific position of the specified road through analyzing the operation influence characteristic distribution diagram of each road operation unit by the road traffic model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311747884.2A CN117455121B (en) | 2023-12-19 | 2023-12-19 | Information management method and system for intelligent road |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311747884.2A CN117455121B (en) | 2023-12-19 | 2023-12-19 | Information management method and system for intelligent road |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117455121A CN117455121A (en) | 2024-01-26 |
| CN117455121B true CN117455121B (en) | 2024-04-02 |
Family
ID=89595126
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311747884.2A Active CN117455121B (en) | 2023-12-19 | 2023-12-19 | Information management method and system for intelligent road |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117455121B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9361797B1 (en) * | 2014-12-11 | 2016-06-07 | Here Global B.V. | Detecting road condition changes from probe data |
| CN112149763A (en) * | 2020-11-25 | 2020-12-29 | 江苏量动信息科技有限公司 | Method and device for improving road surface abnormity detection by using crowdsourcing concept |
| CN112726350A (en) * | 2020-12-25 | 2021-04-30 | 城云科技（中国）有限公司 | Road leveling detection method, device and equipment |
| CN113112790A (en) * | 2021-03-09 | 2021-07-13 | 华东师范大学 | Urban road operation situation monitoring method combined with knowledge graph |
| WO2021186387A1 (en) * | 2020-03-19 | 2021-09-23 | Manav Singal | A method and system for detecting road anomalies |
| CN114187763A (en) * | 2021-12-07 | 2022-03-15 | 罗鑫 | Vehicle driving data screening method and system for intelligent traffic |
| WO2023019761A1 (en) * | 2021-08-20 | 2023-02-23 | 西安电子科技大学 | Road network operation state detection system and method for mixed traffic flow |
| CN115861973A (en) * | 2022-12-28 | 2023-03-28 | 北京星云互联科技有限公司 | Road abnormal state detection method, system, electronic equipment and storage medium |
| CN116013100A (en) * | 2023-01-05 | 2023-04-25 | 中国第一汽车股份有限公司 | Road state early warning method, device, vehicle and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102022100013A1 (en) * | 2022-01-03 | 2023-07-06 | Ford Global Technologies Llc | System and method for vehicle lighting based roadway disruption warning |
-
2023
- 2023-12-19 CN CN202311747884.2A patent/CN117455121B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9361797B1 (en) * | 2014-12-11 | 2016-06-07 | Here Global B.V. | Detecting road condition changes from probe data |
| WO2021186387A1 (en) * | 2020-03-19 | 2021-09-23 | Manav Singal | A method and system for detecting road anomalies |
| CN112149763A (en) * | 2020-11-25 | 2020-12-29 | 江苏量动信息科技有限公司 | Method and device for improving road surface abnormity detection by using crowdsourcing concept |
| CN112726350A (en) * | 2020-12-25 | 2021-04-30 | 城云科技（中国）有限公司 | Road leveling detection method, device and equipment |
| CN113112790A (en) * | 2021-03-09 | 2021-07-13 | 华东师范大学 | Urban road operation situation monitoring method combined with knowledge graph |
| WO2023019761A1 (en) * | 2021-08-20 | 2023-02-23 | 西安电子科技大学 | Road network operation state detection system and method for mixed traffic flow |
| CN114187763A (en) * | 2021-12-07 | 2022-03-15 | 罗鑫 | Vehicle driving data screening method and system for intelligent traffic |
| CN115861973A (en) * | 2022-12-28 | 2023-03-28 | 北京星云互联科技有限公司 | Road abnormal state detection method, system, electronic equipment and storage medium |
| CN116013100A (en) * | 2023-01-05 | 2023-04-25 | 中国第一汽车股份有限公司 | Road state early warning method, device, vehicle and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117455121A (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111862659B (en) | GPS track data matching and complementing method | |
| WO2022141911A1 (en) | Roadside sensing unit-based method for quick recognition of dynamic target point cloud and point cloud segmentation | |
| CN106571046B (en) | Vehicle-road cooperative driving assisting method based on road surface grid system | |
| CN108737955A (en) | LDW/LKA test evaluation system and methods based on virtual lane line | |
| CN110969142B (en) | Abnormal driving scene extraction method based on network-connected vehicle natural driving data | |
| CN110807924A (en) | Multi-parameter fusion method and system based on full-scale full-sample real-time traffic data | |
| CN113434619B (en) | 4G expressway intelligent traffic road condition monitoring system | |
| CN109658693B (en) | Road condition information acquisition and processing method and device | |
| CN109034226A (en) | A kind of track of vehicle clustering method based on graph theory | |
| Doğru et al. | Comparison of clustering techniques for traffic accident detection | |
| CN112258850A (en) | Edge side multi-sensor data fusion system of vehicle-road cooperative system | |
| CN113935158A (en) | Traffic detector optimized layout method for multiple points of highway accidents | |
| CN115223361A (en) | Layout optimization method for roadside sensors in vehicle-road cooperative system | |
| CN110705484A (en) | Method for recognizing illegal behavior of continuously changing lane by using driving track | |
| CN116434523A (en) | Vehicle active safety control method and device based on constraint degree in information perception scene | |
| CN110648538B (en) | Traffic information sensing system and method based on laser radar network | |
| CN117455121B (en) | Information management method and system for intelligent road | |
| CN114329074A (en) | Method and system for detecting traffic energy efficiency of ramp road section | |
| CN114333331A (en) | Method and system for identifying vehicle passing information and vehicle weight of multi-lane bridge | |
| CN103700258A (en) | Road condition information acquisition method and device | |
| CN113516409A (en) | Method and system for identifying rainfall meteorological disaster prone areas in areas along high-speed rail | |
| CN112929421A (en) | Intelligent networked automobile expansion collision early warning system | |
| EP1865740B1 (en) | Determining the position of mobile terminals in a mobile telephone network and uses therefor | |
| CN112415471B (en) | Single-station mobile direction-finding positioning method | |
| CN117952573B (en) | Four-electrical engineering construction management method and system based on GIS data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |