CN118628319B - An urban road planning and design system based on big data - Google Patents
An urban road planning and design system based on big data Download PDFInfo
- Publication number
- CN118628319B CN118628319B CN202410971012.2A CN202410971012A CN118628319B CN 118628319 B CN118628319 B CN 118628319B CN 202410971012 A CN202410971012 A CN 202410971012A CN 118628319 B CN118628319 B CN 118628319B
- Authority
- CN
- China
- Prior art keywords
- planned
- information
- area
- risk
- image
- 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
- 238000013461 design Methods 0.000 title claims description 23
- 238000006073 displacement reaction Methods 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims abstract description 4
- 238000010276 construction Methods 0.000 claims description 35
- 238000000605 extraction Methods 0.000 claims description 22
- 238000012545 processing Methods 0.000 claims description 22
- 238000013500 data storage Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000002955 isolation Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Multimedia (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a city road planning and designing system based on big data, which relates to the technical field of road planning and designing, and comprises an image acquisition module, wherein the image acquisition module acquires images of an area to be planned and acquires images of the area to be planned; the image preprocessing module performs frequency domain conversion on the area image to be planned through a fast Fourier algorithm, and performs preprocessing on the area image to be planned in the frequency domain. According to the method, the risk prediction position is determined by comprehensively analyzing the position information of the work place, the position information of public facilities, the position information of schools and the historical traffic data near the area to be planned, the vehicle braking displacement is determined by calculating the risk prediction position and the position information of the work place, the position information of the public facilities and the position information of schools, and reasonable speed limiting information is set by combining the braking response time of a driver, so that the risk of accidents is reduced, and the problem of road congestion caused by unreasonable limit information is avoided.
Description
Technical Field
The invention relates to the technical field of road planning and design, in particular to an urban road planning and design system based on big data.
Background
Urban roads refer to roads which are communicated with various areas of a city, are used for transportation and pedestrians in the city, are convenient for resident life, work and cultural entertainment activities, and are connected with road outside the city to bear external traffic. The main function of the urban road is transportation, a large number of roads are rebuilt and built in a large number of large cities, the appearance of the cities is changed, and more opportunities are brought for the development of socioeconomic.
The existing urban road is not combined with traffic data and buildings at two sides of the road for comprehensive analysis during design, reasonable speed limiting setting is not carried out on multiple points of accidents, and the speed limiting is unreasonable, so that vehicles run at an excessively low speed, and road congestion is caused.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides the urban road planning and designing system based on big data, and solves the problems that the existing urban road provided in the background art is not combined with traffic data and buildings at two sides of the road for comprehensive analysis during design, reasonable speed limiting setting is not carried out on multiple points of accidents, the speed limiting is unreasonable, vehicles run at too low speed, and road congestion is caused.
In order to achieve the above purpose, the invention adopts the following technical scheme:
An urban road planning and design system based on big data, comprising:
The image acquisition module is used for acquiring images of the area to be planned and acquiring images of the area to be planned;
the image preprocessing module is used for carrying out frequency domain conversion on the image of the area to be planned through a fast Fourier algorithm and preprocessing the image of the area to be planned in the frequency domain, wherein the preprocessing comprises image signal enhancement, image signal denoising, image signal recovery and image normalization;
the data storage module is used for storing historical traffic data of the area to be planned;
The system comprises a feature extraction module, a feature analysis module and a storage module, wherein the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information, the geographic feature information comprises work place position information, public facility position information and school position information, and the traffic feature information comprises travel information and accident information;
the track curve drawing module is used for drawing a curve according to the travel information to obtain a travel track curve;
The risk prediction module is used for analyzing and processing according to the travel track curve and the accident information to obtain a risk prediction position, wherein the risk prediction position comprises a high risk position, a medium risk position and a low risk position;
The road planning module is used for carrying out road design on the area to be planned according to the geographic feature information and the risk prediction position, and determining a road model;
And the construction method design module is used for carrying out construction analysis according to the road model and the travel track curve to obtain a road construction method.
Preferably, the image acquisition module performs image acquisition on the area to be planned, and the step of acquiring the image of the area to be planned specifically includes the following steps:
Acquiring position information of a region to be planned;
Extending the two ends of the position information of the area to be planned through an ant colony algorithm, stopping extending when the two ends of the position information of the area to be planned extend and the first intersection point appears on the road to be passed, and determining the shortest afflux path;
and acquiring images of the area to be planned by acquiring images of two sides of the shortest converging path through the unmanned aerial vehicle.
Preferably, the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and the step of obtaining geographic feature information and traffic feature information specifically includes the following steps:
Performing feature extraction on the preprocessed region image to be planned by using a spot detection algorithm and taking a work place, public facilities and schools as features to acquire geographic feature information;
and carrying out feature extraction on historical traffic data of the area to be planned by taking the vehicle trip and the vehicle accident as features by adopting a hash table, and obtaining traffic feature information.
Preferably, the track curve drawing module performs curve drawing according to travel information, and the step of obtaining the travel track curve specifically includes the following steps:
carrying out fuzzy processing on vehicles and pedestrians in the area image to be planned by adopting a Gaussian fuzzy algorithm to obtain a blank image of the area to be planned;
And drawing a curve in the blank image of the area to be planned according to the travel information, and obtaining a travel track curve.
Preferably, the risk prediction module performs analysis processing according to the travel track curve and the accident information, and the step of obtaining the risk prediction position specifically includes the following steps:
Taking accident information as a characteristic, filling information into blank images of the area to be planned, and obtaining accident occurrence positions;
analyzing and processing according to the accident occurrence position and the travel track curve to obtain risk data;
And judging the risk data and the set value to obtain a risk prediction position.
Preferably, the judging process for the risk data and the set value, and the obtaining the risk prediction position specifically includes the following steps:
judging the risk data and the set value;
If the risk data is greater than or equal to a set first threshold value, outputting a high risk position;
if the risk data is smaller than the set first threshold value and the risk data is larger than or equal to the set second threshold value, outputting a risk position;
And if the risk data is smaller than the set second threshold value, outputting a low risk position.
Preferably, the road planning module performs road design on the area to be planned according to the geographic feature information and the risk prediction position, and the determining the road model specifically includes the following steps:
Acquiring information at two ends of the shortest converging path through the unmanned aerial vehicle to acquire lane number information;
analyzing and processing the area to be planned according to the position information of the working place, the position information of the school and the position information of the public facility, determining speed limit information, and acquiring a vehicle waiting position and a passing path corresponding to the vehicle waiting position;
Setting a warning board according to the risk prediction position in the area to be planned;
and carrying out three-dimensional modeling according to the lane number information, the speed limit information, the vehicle waiting position, the traffic path corresponding to the vehicle waiting position and the warning sign to obtain a road model.
Preferably, the analyzing and processing the area to be planned according to the position information of the working place, the position information of the school and the position information of the public facility, determining the speed limit information, and obtaining the waiting position of the vehicle and the passing path corresponding to the waiting position of the vehicle specifically comprises the following steps:
Calculating according to the risk prediction position, the working place position information, the school position information and the public facility position information to obtain vehicle braking displacement;
Acquiring a driver braking response time;
Analyzing according to the vehicle braking displacement and the driver braking response time, and determining speed limit information;
and analyzing and processing the area to be planned according to the vehicle braking displacement and the speed limit information, and acquiring the vehicle waiting position and a traffic path corresponding to the vehicle waiting position.
Preferably, the construction method design module performs construction analysis according to a road model and a travel track curve, and the road construction method comprises the following steps:
Analyzing the road model according to the travel track curve, and determining the maximum travel route;
taking the maximum driving route as a construction first target;
when the first construction target is constructed, an obvious isolation mark is set to prevent unauthorized personnel from entering a construction area;
when a constructor performs specific operation, the constructor selects a safe position and keeps a correct construction posture.
Compared with the prior art, the invention provides an urban road planning and designing system based on big data, which has the following beneficial effects:
According to the method, the risk prediction position is determined by comprehensively analyzing the position information of the work place, the position information of public facilities, the position information of schools and the historical traffic data near the area to be planned, the vehicle braking displacement is determined by calculating the risk prediction position and the position information of the work place, the position information of the public facilities and the position information of schools, and reasonable speed limiting information is set by combining the braking response time of a driver, so that the risk of accidents is reduced, and the problem of road congestion caused by unreasonable limit information is avoided.
Drawings
FIG. 1 is a block diagram of a system for planning and designing urban roads based on big data according to the present invention;
fig. 2 is a schematic flow chart of acquiring an image of a region to be planned according to the present invention;
FIG. 3 is a schematic flow chart of obtaining geographic feature information and traffic feature information according to the present invention;
fig. 4 is a schematic flow chart of acquiring a travel track curve according to the present invention;
FIG. 5 is a flowchart of acquiring a risk prediction position according to the present invention;
FIG. 6 is a flowchart of acquiring a risk prediction position according to the present invention;
FIG. 7 is a schematic diagram of a flow chart for determining a road model according to the present invention;
FIG. 8 is a schematic flow chart of acquiring a waiting position of a vehicle and a traffic path corresponding to the waiting position of the vehicle according to the present invention;
Fig. 9 is a schematic flow chart of a road construction method according to the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, an urban road planning and designing system based on big data includes:
The image acquisition module is used for acquiring images of the area to be planned and acquiring images of the area to be planned;
the image preprocessing module is used for carrying out frequency domain conversion on the image of the area to be planned through a fast Fourier algorithm and preprocessing the image of the area to be planned in the frequency domain, wherein the preprocessing comprises image signal enhancement, image signal denoising, image signal recovery and image normalization;
the data storage module is used for storing historical traffic data of the area to be planned;
The system comprises a feature extraction module, a feature analysis module and a storage module, wherein the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information, the geographic feature information comprises work place position information, public facility position information and school position information, and the traffic feature information comprises travel information and accident information;
the track curve drawing module is used for drawing a curve according to the travel information to obtain a travel track curve;
The risk prediction module is used for analyzing and processing according to the travel track curve and the accident information to obtain a risk prediction position, wherein the risk prediction position comprises a high risk position, a medium risk position and a low risk position;
The road planning module is used for carrying out road design on the area to be planned according to the geographic feature information and the risk prediction position, and determining a road model;
the construction method design module performs construction analysis according to the road model and the travel track curve to obtain a road construction method;
It can be understood by those skilled in the art that when planning and designing a road, the road needs to be designed in combination with areas with more traffic such as schools, public facilities and office buildings on two sides of the road, in addition, historical traffic data of the road needs to be analyzed to determine accident-prone positions of the road, and reasonable speed limit information is set according to the accident-prone positions and distances among the schools, the public facilities and the office buildings, so that accident occurrence rate can be reduced, road congestion can be avoided, and driving vehicles can pass through quickly.
Referring to fig. 2, the image acquisition module performs image acquisition on an area to be planned, and the step of acquiring an image of the area to be planned specifically includes the following steps:
Acquiring position information of a region to be planned;
Extending the two ends of the position information of the area to be planned through an ant colony algorithm, stopping extending when the two ends of the position information of the area to be planned extend and the first intersection point appears on the road to be passed, and determining the shortest afflux path;
acquiring images of the areas to be planned by acquiring images of two sides of the shortest converging path through the unmanned aerial vehicle;
In this embodiment, the road design in the area to be planned needs to be imported into the already-passed road, so that the area to be planned needs to be extended, when the area to be planned is extended to have an intersection with the already-passed road, the first intersection is selected as the road merging point, the distance from the area to be planned to the first intersection is the shortest merging path, image acquisition is performed on two sides of the shortest merging path, and the working place, public facilities and school in the area to be planned are determined to perform the road planning design.
Referring to fig. 3, the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and the step of obtaining geographic feature information and traffic feature information specifically includes the following steps:
Performing feature extraction on the preprocessed region image to be planned by using a spot detection algorithm and taking a work place, public facilities and schools as features to acquire geographic feature information;
The edge characteristics of a work place, public facilities and schools are enhanced through a Gaussian filter, so that characteristic extraction of an image of a region to be planned is more accurate;
The method comprises the steps that a hash table is adopted, and vehicle travel and vehicle accidents are used as characteristics to conduct characteristic extraction on historical traffic data of an area to be planned, so that traffic characteristic information is obtained;
In this embodiment, accurate positioning is required for a work place, public facilities and schools in an image of an area to be planned, geographic feature information is determined, a road is designed according to the geographic feature information, in addition, accident-prone positions in the area to be planned are determined by combining historical traffic data of the area to be planned, the accident-prone positions are marked, and after the road is built, the accident occurrence rate can be reduced.
Referring to fig. 4, the track curve drawing module performs curve drawing according to travel information, and the step of obtaining a travel track curve specifically includes the following steps:
carrying out fuzzy processing on vehicles and pedestrians in the area image to be planned by adopting a Gaussian fuzzy algorithm to obtain a blank image of the area to be planned;
drawing a curve in a blank image of the area to be planned according to the travel information to obtain a travel track curve;
the curve can intuitively reflect the distribution condition of data, so that the travel track curve is drawn according to travel information, the vehicle traveling condition can be intuitively seen, the travel track curve is drawn in an image of a region to be planned for improving the accuracy of road design, meanwhile, the image of the region to be planned is subjected to fuzzy processing for avoiding interference of other factors in the image of the region to be planned on the travel track curve, and vehicle features and pedestrian features in the region to be planned are blurred.
Referring to fig. 5, the risk prediction module performs analysis processing according to a travel track curve and accident information, and the step of obtaining a risk prediction position specifically includes the following steps:
Taking accident information as a characteristic, filling information into blank images of the area to be planned, and obtaining accident occurrence positions;
analyzing and processing according to the accident occurrence position and the travel track curve to obtain risk data;
Judging the risk data and the set value to obtain a risk prediction position;
As will be appreciated by those skilled in the art, there is an injury range in the accident location, so that the accident location and the travel track curve are combined and analyzed, the central point close to the accident location is a high risk location, the central point far from the accident location is a low risk location, and the first threshold, the second threshold and the third threshold are set according to the travel track curve and the distance between the accident location.
Referring to fig. 6, the risk data and the set value are judged, and the step of obtaining the risk prediction position specifically includes the following steps:
judging the risk data and the set value;
If the risk data is greater than or equal to a set first threshold value, outputting a high risk position;
if the risk data is smaller than the set first threshold value and the risk data is larger than or equal to the set second threshold value, outputting a risk position;
And if the risk data is smaller than the set second threshold value, outputting a low risk position.
Referring to fig. 7, the road planning module performs road design on an area to be planned according to geographic feature information and a risk prediction position, and the determining a road model specifically includes the following steps:
Acquiring information at two ends of the shortest converging path through the unmanned aerial vehicle to acquire lane number information;
analyzing and processing the area to be planned according to the position information of the working place, the position information of the school and the position information of the public facility, determining speed limit information, and acquiring a vehicle waiting position and a passing path corresponding to the vehicle waiting position;
Setting a warning board according to the risk prediction position in the area to be planned;
and carrying out three-dimensional modeling according to the lane number information, the speed limit information, the vehicle waiting position, the traffic path corresponding to the vehicle waiting position and the warning sign to obtain a road model.
Referring to fig. 8, according to the work place position information, school position information and public facility position information, the area to be planned is analyzed and processed, speed limit information is determined, and the vehicle waiting position and the traffic path corresponding to the vehicle waiting position are obtained, specifically including the following steps:
specifically, the method comprises the following steps:
Calculating according to the risk prediction position, the working place position information, the school position information and the public facility position information to obtain vehicle braking displacement;
Acquiring a driver braking response time;
Analyzing according to the vehicle braking displacement and the driver braking response time, and determining speed limit information;
Analyzing and processing the area to be planned according to the vehicle braking displacement and the speed limit information, and acquiring a vehicle waiting position and a passing path corresponding to the vehicle waiting position;
It will be appreciated by those skilled in the art that the work site, school and public facility are required to be far from the accident site, however, it cannot be guaranteed that the work site, school and public facility are all far from the accident site, and when the work site, school and public facility are close to the accident site, speed limit is required to be carried out on the road, so that the work site, school and public facility and the accident site are calculated, the distance between the work site, school and public facility and the accident site is determined, and reasonable speed limit information is set for the road in combination with the driver braking response time, the accident rate is reduced, and road congestion is avoided.
Referring to fig. 9, the construction method design module performs construction analysis according to a road model and a travel track curve, and the road construction method specifically includes the following steps:
Analyzing the road model according to the travel track curve, and determining the maximum travel route;
taking the maximum driving route as a construction first target;
when the first construction target is constructed, an obvious isolation mark is set to prevent unauthorized personnel from entering a construction area;
When a constructor performs specific operation, selecting a safe position and keeping a correct construction posture;
In this embodiment, when the travel track curves are denser, it is explained that the preference degree of the driver for the area is higher, therefore, the maximum travel route is taken as the first target of construction, when the construction of the first target of construction is completed, the driver can walk upright, and the boring degree of road construction of the driver can be reduced.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. An urban road planning and design system based on big data, which is characterized by comprising:
The image acquisition module is used for acquiring images of the area to be planned and acquiring images of the area to be planned;
the image preprocessing module is used for carrying out frequency domain conversion on the image of the area to be planned through a fast Fourier algorithm and preprocessing the image of the area to be planned in the frequency domain, wherein the preprocessing comprises image signal enhancement, image signal denoising, image signal recovery and image normalization;
the data storage module is used for storing historical traffic data of the area to be planned;
The system comprises a feature extraction module, a feature analysis module and a storage module, wherein the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned to obtain geographic feature information and traffic feature information, the geographic feature information comprises work place position information, public facility position information and school position information, and the traffic feature information comprises travel information and accident information;
the track curve drawing module is used for drawing a curve according to the travel information to obtain a travel track curve;
The risk prediction module is used for analyzing and processing according to the travel track curve and the accident information to obtain a risk prediction position, wherein the risk prediction position comprises a high risk position, a medium risk position and a low risk position;
The road planning module is used for carrying out road design on the area to be planned according to the geographic feature information and the risk prediction position, and determining a road model;
The road planning module carries out road design on the area to be planned according to the geographic feature information and the risk prediction position, and the road model determining step specifically comprises the following steps:
Acquiring information at two ends of the shortest converging path through the unmanned aerial vehicle to acquire lane number information;
analyzing and processing the area to be planned according to the position information of the working place, the position information of the school and the position information of the public facility, determining speed limit information, and acquiring a vehicle waiting position and a passing path corresponding to the vehicle waiting position;
Setting a warning board according to the risk prediction position in the area to be planned;
carrying out three-dimensional modeling according to the lane number information, the speed limit information, the vehicle waiting position, the traffic path corresponding to the vehicle waiting position and the warning sign to obtain a road model;
the method comprises the steps of analyzing and processing a region to be planned according to the position information of a working place, the position information of a school and the position information of public facilities, determining speed limit information, and obtaining a vehicle waiting position and a passing path corresponding to the vehicle waiting position specifically comprises the following steps:
Calculating according to the risk prediction position, the working place position information, the school position information and the public facility position information to obtain vehicle braking displacement;
Acquiring a driver braking response time;
Analyzing according to the vehicle braking displacement and the driver braking response time, and determining speed limit information;
Analyzing and processing the area to be planned according to the vehicle braking displacement and the speed limit information, and acquiring a vehicle waiting position and a passing path corresponding to the vehicle waiting position;
And the construction method design module is used for carrying out construction analysis according to the road model and the travel track curve to obtain a road construction method.
2. The urban road planning and design system based on big data according to claim 1, wherein the image acquisition module performs image acquisition on the area to be planned, and the step of acquiring the image of the area to be planned specifically comprises the following steps:
Acquiring position information of a region to be planned;
Extending the two ends of the position information of the area to be planned through an ant colony algorithm, stopping extending when the two ends of the position information of the area to be planned extend and the first intersection point appears on the road to be passed, and determining the shortest afflux path;
and acquiring images of the area to be planned by acquiring images of two sides of the shortest converging path through the unmanned aerial vehicle.
3. The urban road planning and designing system based on big data according to claim 1, wherein the feature extraction module performs feature extraction on the preprocessed image of the area to be planned and the historical traffic data of the area to be planned, and the step of obtaining the geographic feature information and the traffic feature information specifically includes the following steps:
Performing feature extraction on the preprocessed region image to be planned by using a spot detection algorithm and taking a work place, public facilities and schools as features to acquire geographic feature information;
and carrying out feature extraction on historical traffic data of the area to be planned by taking the vehicle trip and the vehicle accident as features by adopting a hash table, and obtaining traffic feature information.
4. The urban road planning and designing system based on big data according to claim 1, wherein the track curve drawing module performs curve drawing according to travel information, and the step of obtaining the travel track curve specifically comprises the following steps:
carrying out fuzzy processing on vehicles and pedestrians in the area image to be planned by adopting a Gaussian fuzzy algorithm to obtain a blank image of the area to be planned;
And drawing a curve in the blank image of the area to be planned according to the travel information, and obtaining a travel track curve.
5. The urban road planning and designing system based on big data according to claim 1, wherein the risk prediction module performs analysis and processing according to a travel track curve and accident information, and the step of obtaining a risk prediction position specifically includes the following steps:
Taking accident information as a characteristic, filling information into blank images of the area to be planned, and obtaining accident occurrence positions;
analyzing and processing according to the accident occurrence position and the travel track curve to obtain risk data;
And judging the risk data and the set value to obtain a risk prediction position.
6. The urban road planning and design system based on big data according to claim 5, wherein the judging and processing of the risk data and the set value, and the obtaining of the risk prediction position specifically comprises the following steps:
judging the risk data and the set value;
If the risk data is greater than or equal to a set first threshold value, outputting a high risk position;
if the risk data is smaller than the set first threshold value and the risk data is larger than or equal to the set second threshold value, outputting a risk position;
And if the risk data is smaller than the set second threshold value, outputting a low risk position.
7. The urban road planning and designing system based on big data according to claim 1, wherein the construction method designing module performs construction analysis according to a road model and a travel track curve, and the road construction method comprises the following steps:
Analyzing the road model according to the travel track curve, and determining the maximum travel route;
taking the maximum driving route as a construction first target;
when the first construction target is constructed, an obvious isolation mark is set to prevent unauthorized personnel from entering a construction area;
when a constructor performs specific operation, the constructor selects a safe position and keeps a correct construction posture.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410971012.2A CN118628319B (en) | 2024-07-19 | 2024-07-19 | An urban road planning and design system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410971012.2A CN118628319B (en) | 2024-07-19 | 2024-07-19 | An urban road planning and design system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118628319A CN118628319A (en) | 2024-09-10 |
| CN118628319B true CN118628319B (en) | 2025-01-28 |
Family
ID=92603395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410971012.2A Active CN118628319B (en) | 2024-07-19 | 2024-07-19 | An urban road planning and design system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118628319B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119273393B (en) * | 2024-12-09 | 2025-02-25 | 南京信息工程大学 | School site selection method and device based on fast convergence ant colony algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106128095A (en) * | 2016-06-13 | 2016-11-16 | 东南大学 | A kind of through street isolates the variable speed-limiting control method of bottleneck road |
| CN113361997A (en) * | 2021-06-04 | 2021-09-07 | 南京大学 | Dangerous waste transportation path real-time planning method based on risk minimization |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014207558A2 (en) * | 2013-06-27 | 2014-12-31 | Scope Technologies Holdings Limited | Onboard vehicle accident detection and damage estimation system and method of use |
| KR20180073852A (en) * | 2016-12-23 | 2018-07-03 | 주식회사 웨이브엠 | the intelligent guidance system of driving attention information based on social big data |
| CN117593167B (en) * | 2024-01-18 | 2024-04-12 | 山东国建土地房地产评估测绘有限公司 | Intelligent city planning management method and system based on big data |
| CN117953715A (en) * | 2024-03-27 | 2024-04-30 | 陕西天诚软件有限公司 | Smart city traffic management system based on big data analysis |
-
2024
- 2024-07-19 CN CN202410971012.2A patent/CN118628319B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106128095A (en) * | 2016-06-13 | 2016-11-16 | 东南大学 | A kind of through street isolates the variable speed-limiting control method of bottleneck road |
| CN113361997A (en) * | 2021-06-04 | 2021-09-07 | 南京大学 | Dangerous waste transportation path real-time planning method based on risk minimization |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118628319A (en) | 2024-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2018068653A1 (en) | Point cloud data processing method and apparatus, and storage medium | |
| CN108345822B (en) | Point cloud data processing method and device | |
| EP3016086B1 (en) | Negative image for sign placement detection | |
| EP3719714B1 (en) | Machine learning system for classifying an area as vehicle way | |
| US8184861B2 (en) | Feature information management apparatuses, methods, and programs | |
| US11335191B2 (en) | Intelligent telematics system for defining road networks | |
| CN113421432B (en) | Traffic restriction information detection method and device, electronic equipment and storage medium | |
| CN107305131A (en) | Navigation optimization centered on node | |
| US11335189B2 (en) | Method for defining road networks | |
| US11341846B2 (en) | Traffic analytics system for defining road networks | |
| CN114518122B (en) | Driving navigation method, device, computer equipment, storage medium and computer program product | |
| CN118628319B (en) | An urban road planning and design system based on big data | |
| CN114596704A (en) | Traffic event processing method, device, equipment and storage medium | |
| CN114692713A (en) | A method and device for evaluating driving behavior of an autonomous vehicle | |
| CN115060249A (en) | Electronic map construction method, device, equipment and medium | |
| CN115995151B (en) | Network vehicle-closing abnormal behavior detection method applied to city management | |
| JP4223309B2 (en) | Route guidance device, route guidance method, and computer program | |
| US11816989B2 (en) | Identification of connection patterns on the basis of trajectory data | |
| US12235126B2 (en) | Method and apparatus for determining window damage indicators | |
| EP3919860A1 (en) | Intelligent telematics system for defining road networks | |
| EP3922947A2 (en) | Traffic analytics system for defining road networks | |
| CN117274303A (en) | Intelligent tracking method and system for vehicle track | |
| US20230332911A1 (en) | Method and apparatus for determining roadworks locations | |
| CN114220290A (en) | Method and system for automatically searching parking space and automatically parking vehicle | |
| EP3913551B1 (en) | Method for defining road networks |
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 |