CN114282033A - Deviation correction and intelligent road disease reporting system based on GPS - Google Patents
Deviation correction and intelligent road disease reporting system based on GPS Download PDFInfo
- Publication number
- CN114282033A CN114282033A CN202210195620.XA CN202210195620A CN114282033A CN 114282033 A CN114282033 A CN 114282033A CN 202210195620 A CN202210195620 A CN 202210195620A CN 114282033 A CN114282033 A CN 114282033A
- Authority
- CN
- China
- Prior art keywords
- gps
- disease
- point
- classification
- database
- 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.)
- Granted
Links
- 201000010099 disease Diseases 0.000 title claims abstract description 153
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 153
- 238000012937 correction Methods 0.000 title claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- 238000012800 visualization Methods 0.000 claims abstract description 23
- 230000000007 visual effect Effects 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 4
- 101100182247 Caenorhabditis elegans lat-1 gene Proteins 0.000 claims description 3
- 101100182248 Caenorhabditis elegans lat-2 gene Proteins 0.000 claims description 3
- 238000012550 audit Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000012384 transportation and delivery Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 13
- 238000005516 engineering process Methods 0.000 description 26
- 238000007726 management method Methods 0.000 description 16
- 238000000034 method Methods 0.000 description 12
- 238000001514 detection method Methods 0.000 description 10
- 238000011161 development Methods 0.000 description 8
- 238000007689 inspection Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000010276 construction Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism 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
- 238000007789 sealing Methods 0.000 description 1
- 238000012031 short term test Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Landscapes
- Navigation (AREA)
- Instructional Devices (AREA)
Abstract
The invention provides a deviation rectifying and road disease intelligent reporting system based on a GPS, and relates to the technical field of intelligent information. The system provided by the invention comprises a reporting terminal and an intelligent analysis cloud; the intelligent analysis cloud is provided with a picture identification and classification unit, a GPS (global positioning system) deviation correction algorithm unit, a GPS basic database, a GIS network database and a GIS map visual analysis system; the image identification and classification unit can perform image identification and classification on the road image data; the GPS deviation correction algorithm unit can carry out GPS route positioning and positioning deviation correction on the GPS positioning data to obtain accurate positioning coordinates; the GPS basic database is used for storing basic data required by GPS positioning; the GIS network database is used for storing GIS network data; the GIS map visualization analysis system is used for visually displaying a spatial information map of the reported information of the reporting terminal; the system can identify the road condition and perform accurate positioning; the maintenance work plan is favorably and reasonably arranged, the labor cost is reduced, and the output efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent information, in particular to a deviation rectifying and road disease intelligent reporting system based on a GPS.
Background
The rural highway plays an extremely important role in the process of promoting economic and social progress, is used as one of main traffic outgoing carriers for connecting rural areas with the outside, and can drive the development of rural economy. Along with the promotion of the strategy of village promotion in recent years, the construction scale of various rural basic services is continuously increased, and the effect is remarkable particularly in the aspect of road construction. However, in view of the current practical situation, various diseases often occur in the process of putting rural highways into operation in the later period, which greatly affects the use functions and service lives of the rural highways, and the damage to the highways is continuously increased along with the trend that vehicles in rural areas rise year by year, so that the analysis of road surface diseases is necessary, and matched maintenance measures are taken, thereby protecting the safe operation of the rural highways.
The construction condition of 'four good rural villages' in the 'thirteen-five' period is that the country accumulatively completes the new reconstruction of 138.8 kilometers of rural highways, the total mileage of rural highways in the country reaches 420 kilometers, the total mileage of rural highways in Sichuan province reaches 34.7 kilometers, and the rural highways account for 83.8 percent of the total mileage of the highways. The rapid development of rural highways is reflected from the data, the highway maintenance cost is continuously increased, the investment of the project can meet the maintenance capital requirement of the rapid development of rural areas, and the maintenance development of the rural highways is greatly promoted. Therefore, we need to introduce new technologies and new means to enable rural highway management. Insist on the intelligent development, promote to apply new technology, new means enable rural highway management maintenance work. The application of new technologies such as 5G, Beidou, Internet of things, big data, satellite remote sensing and the like is strengthened, and the rural highway management efficiency and maintenance level are continuously improved.
The informatization level of rural highway management is continuously improved, and a 'quick detection technology' is also proposed for the first time; the method has the advantages of promoting the digitization of the basic information of the rural highway, strengthening the application of new technologies such as internet, satellite remote sensing, rapid detection and the like, establishing a long-acting mechanism for data acquisition and processing, perfecting the comprehensive supervision capability of the rural highway, strengthening the information resource sharing and exchange and improving the management efficiency of the rural highway.
For an auxiliary driving system of an intelligent automobile, the key point is that the method can accurately identify and detect a path target and a vehicle environment, and makes reasonable judgment through data analysis. Road conditions and driving environments are complex and variable, and generally include road signs, lane lines, traffic signs, obstacles, pedestrians, vehicles, traffic signals and the like. How to quickly and accurately detect a target path and a driving state in an effective time becomes a difficult point for researching the automobile safety auxiliary driving technology. Along with the rapid development of the vision technology, the machine vision is more accurate in target identification and detection, the quantity of acquired information is large, the method does not depend on the change of the road shape, multi-target detection can be performed, the maintenance cost is low, and the storage is convenient. Therefore, vision technology represents a great advantage in road identification and tracking of smart cars. The research of machine vision in the field of intelligent transportation has been carried out for a long time abroad, and related mature products come out, but the research in the aspect of China is relatively late, the technology is not mature enough, and the related products are not many. Therefore, research on intelligent car-assistant driving technology based on vision technology is urgently carried out.
The machine vision is to acquire images through a camera, send the images to a computer for processing, and acquire characteristic information contained in the images so as to realize target recognition and detection. With the rapid development of image processing technology, machine vision has been widely applied in various fields such as detection and monitoring, industrial production, visual navigation, human-computer interaction and the like, and great convenience is brought to the production and life of people. Especially in the field of intelligent automobiles, the application of vision technology brings great changes to the automobile revolution.
The defects and shortcomings of the prior art are as follows: 1. the GPS error of the vehicle-mounted equipment is large, and specific diseases cannot be accurately displayed on a map. 2. Road diseases are not reported in time, and the reported positions of the diseases are too many, so that the data volume is too large, invalid data are redundant, the reading and analyzing efficiency is completely influenced, and the visualization of data visual display is influenced. 3. The labor cost required by highway disease management is high, and the efficiency is low.
Therefore, there is a need to provide a new intelligent reporting system for road diseases to solve one or more of the above technical problems.
Disclosure of Invention
In order to solve one or more technical problems, the invention provides a deviation rectifying and road disease intelligent reporting system based on a GPS, which comprises a reporting terminal and an intelligent analysis cloud.
Specifically, the reporting terminal is deployed on mobile and/or vehicle-mounted intelligent equipment and establishes communication connection with the intelligent analysis cloud through a communication network; the reporting terminal can collect/upload GPS positioning data and road image data in real time and receive/upload data through a data packet and an intelligent analysis cloud.
More specifically, the intelligent analysis cloud is provided with a picture identification and classification unit, a GPS (global positioning system) deviation correction algorithm unit, a GPS basic database, a GIS network database and a GIS map visualization analysis system.
In detail, the image identification and classification unit is used for receiving the road image data uploaded by the report terminal and carrying out image identification and classification on the road image data to obtain a road disease identification result; the GPS deviation correction algorithm unit is used for receiving the GPS positioning data uploaded by the reporting terminal, and carrying out GPS route positioning and positioning deviation correction on the GPS positioning data to obtain an accurate positioning coordinate; the GPS basic database is used for storing basic data required by GPS positioning; the GIS network database is used for storing GIS network data; the GIS map visualization analysis system is used for visually displaying the spatial information map of the reported information of the reporting terminal.
As a further solution, the GPS basic database and the GIS network database both use mysql database as storage database and Redis database as cache database; the Redis database is deployed with Redis Geo and performs data processing through Redis message queue data.
As a further solution, the GPS deviation correction algorithm unit obtains the offset constant coefficient of the positioning road section by the following steps
：
S1, receiving the GPS positioning data uploaded by the report terminal;
s2 setting the latitude and longitude of the current location point as upload point P (X)0，Y0）；
S3 finding nearby positioning point A (X) through Redis Geo1，Y1) The positioning point A belongs to the route L;
s4 matches any value of two forward and backward points through the positioning point A as the positioning point B (X)2，Y2）；
S5 finds the vector AB (X) between the anchor A and the anchor B2-X1，Y2-Y1) Wherein, in the step (A),
；
s6, making a perpendicular line from the uploading point P to the L line, and setting the intersection point as a perpendicular point Q (X, Y);
s7 finds the upload point P and the vector PQ (X-X)0，Y-Y0) Wherein, in the step (A),
；
s8 is represented by vector PQ perpendicular to vector AB, i.e.: the mode of the projection of the vector PQ on the vector AB is 0, the dot product of the projection is 0, and the coordinate of a Q point is calculated; wherein the content of the first and second substances,
；
s9, judging whether the point Q is on the straight line L line, namely calculating the formula:
whether the result is true or not; if yes, marking the point Q as a deviation correcting point Q corresponding to the uploading point P;
s10 calculating the distance S between the positioning point A and the deviation correcting point Q1Wherein, in the step (A),
；
s11, acquiring longitude and latitude of each point;
s12 calculates the actual distance S between locating point A longitude and latitude lat1, long 1 and uploading point P longitude and latitude lat2, long 22The calculation formula is as follows:
s13 reaction of S1And S2An offset is introduced, and an offset M of the upload point P is calculated, where M = S1/S2；
S14 repeating S1-S13 to obtain average value of multiple offsets of L line as regular offset constant coefficient
The formula is as follows:
(ii) a Wherein n is the measurement times, i is the measurement number, and the obtained offset constant coefficient
。
As a further solution, the GPS deviation correction algorithm unit judges whether the GPS positioning data deviates from the GIS network data by the following steps:
a1 making a vertical line from the uploading point P to the vector AB to obtain a vertical point Q;
a2 judges whether the vertical point Q is on the segment AB: i.e. formula of calculation
Whether the result is true or not; if yes, the vertical point Q is in the AB line segment; if the vertical point Q is not in the AB line segment, the vertical point Q is not in the AB line segment;
a3 using offset constant coefficient if vertical point Q is not in AB line segment
Calculating a coordinate point of the vertical point Q in the AB line segment; if the vertical point Q is in the AB line segment, directly calculating the coordinate of the vertical point Q, and executing the next step;
a4, searching whether the coordinate corresponding to the vertical point Q exists in a GPS basic database, wherein a GPS repetition threshold is preset, and if the distance between the two points is greater than the GPS repetition threshold, determining the two different coordinates; if the distance between the two points is not greater than the GPS repetition threshold, the two points are regarded as two identical coordinates;
a5, if the coordinate corresponding to the vertical point Q already exists in the GPS basic database, only updating the reported information corresponding to the coordinate; and if the coordinate corresponding to the vertical point Q does not exist in the GPS basic database, newly building and storing the coordinate into the GPS basic database, and newly building the report information corresponding to the coordinate.
As a further solution, the picture recognition and classification unit is an AI-based picture recognition and classification unit, and trains the model through a disease picture classification training library to obtain the characteristic values of each disease classification, and the steps are as follows:
b1, collecting road disease pictures and carrying out classification treatment to obtain a disease picture classification training library;
b2, inputting the disease picture classification training library into a picture recognition and classification unit according to classification and extracting features;
b3, obtaining the characteristics of the road diseases and calculating the characteristic values to obtain the classification characteristic values of the diseases;
b4, adding the disease classification characteristic values corresponding to various road diseases into a disease classification characteristic value database.
As a further solution, the picture identification and classification unit obtains the classification result by:
c1, receiving and reporting road image data uploaded by the terminal;
c2 extracting road image data characteristics through an image identification and classification unit and calculating disease image characteristic values;
c3, writing the characteristic value of the disease picture into a Redis database for caching;
c4, writing the disease classification characteristic value database into a Redis database for caching;
c5, comparing and analyzing the characteristic value of the disease picture with the characteristic value database of the disease classification;
c6, if the disease picture characteristic value is matched with the disease classification characteristic value database, outputting a disease classification result; and if the matching is not established, the disease classification result is the unidentified disaster.
As a further solution, the GIS map visualization analysis system is a layered, dynamic and delivery map visualization system; the GIS map visualization analysis system comprises a GIS road network layer, a disease layer and a patrol layer; the GIS road network map layer comprises routes, road sections, bridges, tunnels, culverts, side slopes, intercommunication areas, traffic implementation, traffic volume and data files; the disease layer shows specific positions by a disease chart, wherein the specific positions comprise a disease audit state, a disease disposal state, a disease site, disease classification, a reporting unit, a disease picture, a disease area and disease volume information; the patrol map layer is used for displaying daily patrol real-time tracks of patrol personnel, and comprises patrol tracks, patrol times and patrol mileage.
Compared with the prior art, the system for correcting the deviation and intelligently reporting the road diseases based on the GPS has the following beneficial effects:
1. the intelligent reporting system provided by the invention can identify the road surface condition and can report, check and process the diseases; maintenance work plans are reasonably arranged, labor cost is reduced, and output efficiency is improved;
2. the method can accurately display the disease position on the map by combining a GPS (global positioning system) deviation correction algorithm unit with a visualization technology, and accurately display the disease on the Gaode map, wherein the uploaded longitude and latitude and basic road data find out the nearest coordinate of the GPS position through the Redis Geo, the Redis Geo can conveniently calculate the point of the nearest distance, and then the deviation correction is performed through the deviation correction algorithm, so that the matching line which is completely matched with the GIS network database is realized, and the precise display on the Gaode map is convenient;
3. the GIS map visualization analysis system used by the invention is a layered, dynamic and delivered map visualization system, and comprises a GIS road network layer, a disease layer and an inspection layer; the user can intuitively, quickly, accurately and multi-level know the road disease condition; the working efficiency is improved;
4. the method comprises the steps of processing and analyzing reported diseases through a picture recognition and classification technology, simultaneously performing GPS (global positioning system) deviation correction processing on reported GPS longitude and latitude, reporting to an intelligent analysis cloud, and visually displaying specific positions of the diseases through a GIS (geographic information system) map visual analysis system; the inspection cost and the detection cost are reduced to a great extent, the road inspection efficiency is improved, and intelligent management such as intellectualization, automation, visualization and the like is realized.
Drawings
Fig. 1 is a preferred system block diagram of a system for correcting errors and intelligently reporting road diseases based on a GPS according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a preferred flow chart of a GPS error correction algorithm unit according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a preferred embodiment of a picture recognition and classification unit according to the present invention;
fig. 4 is a schematic diagram of a preferred flow of reporting a road disease according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
As shown in fig. 1 to 4, in order to solve one or more of the above technical problems, the present embodiment provides a system for deviation correction and intelligent reporting of a road fault based on a GPS, which includes a reporting terminal and an intelligent analysis cloud.
Specifically, the reporting terminal is deployed on mobile and/or vehicle-mounted intelligent equipment and establishes communication connection with the intelligent analysis cloud through a communication network; the reporting terminal can collect/upload GPS positioning data and road image data in real time and receive/upload data through a data packet and an intelligent analysis cloud.
More specifically, the intelligent analysis cloud is provided with a picture identification and classification unit, a GPS (global positioning system) deviation correction algorithm unit, a GPS basic database, a GIS network database and a GIS map visualization analysis system.
In detail, the image identification and classification unit is used for receiving the road image data uploaded by the report terminal and carrying out image identification and classification on the road image data to obtain a road disease identification result; the GPS deviation correction algorithm unit is used for receiving the GPS positioning data uploaded by the reporting terminal, and carrying out GPS route positioning and positioning deviation correction on the GPS positioning data to obtain an accurate positioning coordinate; the GPS basic database is used for storing basic data required by GPS positioning; the GIS network database is used for storing GIS network data; the GIS map visualization analysis system is used for visually displaying the spatial information map of the reported information of the reporting terminal.
It should be noted that: the bottom layer main core technology of the intelligent reporting system based on GPS deviation correction and road diseases provided by the implementation adopts a GPS deviation correction technology and a disease picture identification and classification technology; the GPS deviation rectifying technology based on Redis Geo aims to solve the problems of inaccuracy and overlarge offset of the position of a damaged GPS uploaded by equipment. The disease reporting is mainly realized through a mobile phone app and a vehicle-mounted device AI, namely a mobile and/or vehicle-mounted intelligent device. The mobile phone app reports in a voice and picture mode, a picture recognition and classification technology is adopted to process and analyze reported diseases, GPS deviation correction processing is carried out on reported GPS longitude and latitude, intelligent road disease reporting is carried out on a road surface by a vehicle carrying AI algorithm device, and specific positions of the diseases are visually displayed through a Gade map layer. The inspection cost and the detection cost are reduced to a great extent, the road inspection efficiency is improved, and intelligent management such as intellectualization, automation, visualization and the like is realized.
The intelligent inspection equipment is installed on operating vehicles (such as buses, cleaning vehicles, rural passenger vehicles and the like) and law enforcement vehicles, and vehicle-mounted equipment AI is loaded to realize rapid detection and presentation of a target value of a detected road surface. The user can realize the short-term test to the road of process in step when not disturbing the daily operation of vehicle, and main detection objective is data such as road surface crack, road surface hole groove, road surface wave hug, road surface running quality index RQI to show the testing result adoption visual mode.
As a further solution, the GPS basic database and the GIS network database both use mysql database as storage database and Redis database as cache database; the Redis database is deployed with Redis Geo and performs data processing through Redis message queue data.
It should be noted that: the distance between the disease reported GPS data and the point on the L line can be obtained by utilizing the GEO geographic positioning calculation of redis, the longitude and latitude (coordinates) of the point on the L line stored in the GIS network database are calculated by the GEO to obtain the distance. And the GPS basic database and the GIS road network basic database are stored by adopting mysql database. The cache database adopts a Redis database. And simultaneously, the Redis message queue is used for data processing and the Redis Geo is used for calculating the longitude and the latitude. The GIS road network basic database is a GIS network database of a road basic database of a scientific research institute of the department of transportation, leads out GIS network data of a certain area, and obtains the GIS network database through data cleaning. Reference data for a GPS deskew algorithm.
As a further solution, as shown in FIG. 2, the GPS deviation correction algorithm unit obtains the offset constant coefficient of the positioning road section by the following steps
：
S1, receiving the GPS positioning data uploaded by the report terminal;
s2 setting the latitude and longitude of the current location point as upload point P (X)0，Y0）；
S3 finding nearby through Redis GeoLocation point A (X)1，Y1) The positioning point A belongs to the route L;
s4 matches any value of two forward and backward points through the positioning point A as the positioning point B (X)2，Y2）；
S5 finds the vector AB (X) between the anchor A and the anchor B2-X1，Y2-Y1) Wherein,
；
s6, making a perpendicular line from the uploading point P to the L line, and setting the intersection point as a perpendicular point Q (X, Y);
s7 finds the upload point P and the vector PQ (X-X)0，Y-Y0) Wherein, in the step (A),
；
s8 is represented by vector PQ perpendicular to vector AB, i.e.: the mode of the projection of the vector PQ on the vector AB is 0, the dot product of the projection is 0, and the coordinate of a Q point is calculated; wherein the content of the first and second substances,
；
s9, judging whether the point Q is on the straight line L line, namely calculating the formula:
whether the result is true or not; if yes, marking the point Q as a deviation correcting point Q corresponding to the uploading point P;
s10 calculating the distance S between the positioning point A and the deviation correcting point Q1Wherein, in the step (A),
；
s11, acquiring longitude and latitude of each point;
s12 calculates the actual distance S between locating point A longitude and latitude lat1, long 1 and uploading point P longitude and latitude lat2, long 22The calculation formula is as follows:
s13 reaction of S1And S2An offset is introduced, and an offset M of the upload point P is calculated, where M = S1/S2；
S14 repeating S1-S13 to obtain average value of multiple offsets of L line as regular offset constant coefficient
The formula is as follows:
(ii) a Wherein n is the measurement times, i is the measurement number, and the obtained offset constant coefficient
。
As a further solution, as shown in fig. 2, the GPS deviation rectifying algorithm unit determines whether the GPS positioning data deviates from the GIS network data by the following steps:
a1 making a vertical line from the uploading point P to the vector AB to obtain a vertical point Q;
a2 judges whether the vertical point Q is on the segment AB: i.e. formula of calculation
Whether the result is true or not; if yes, the vertical point Q is in the AB line segment; if the vertical point Q is not in the AB line segment, the vertical point Q is not in the AB line segment;
a3 using offset constant coefficient if vertical point Q is not in AB line segment
Calculating a coordinate point of the vertical point Q in the AB line segment; if the vertical point Q is in the AB line segment, directly calculating the coordinate of the vertical point Q, and executing the next step;
here, it should be noted that: multiplying the distance from the uploading point P to the positioning point A by an offset constant coefficient
The distance from the vertical point Q to the point A in the AB line segment can be obtained, and the linear equation can be solved by knowing the coordinates of the two points A, B; the coordinate of the vertical point Q can be obtained by the simultaneous connection of the linear equation and the distance from the vertical point Q to the point A.
A4, searching whether the coordinate corresponding to the vertical point Q exists in a GPS basic database, wherein a GPS repetition threshold is preset, and if the distance between the two points is greater than the GPS repetition threshold, determining the two different coordinates; if the distance between the two points is not greater than the GPS repetition threshold, the two points are regarded as two identical coordinates;
a5, if the coordinate corresponding to the vertical point Q already exists in the GPS basic database, only updating the reported information corresponding to the coordinate; and if the coordinate corresponding to the vertical point Q does not exist in the GPS basic database, newly building and storing the coordinate into the GPS basic database, and newly building the report information corresponding to the coordinate.
It should be noted that: in the embodiment, the GPS deviation correction algorithm unit can be combined with the visualization technology to accurately display the disease position on the map and accurately display the disease on the Gade map. The GPS deviation correction step comprises a GPS matching line, GPS vertical point calculation, GPS constant coefficient calculation and GPS duplication removal. Firstly, the equipment for reporting diseases reports longitude and latitude information. The latest coordinates of the GPS position are found out through the uploaded longitude and latitude and basic road data through the Redis Geo, the Redis Geo can conveniently calculate the point of the closest distance, the number of the returned records is set to be limited to be 10, the more the number of the closest points matched to the line is, the equipment point is marked on the line, and then the deviation is corrected through a deviation correction algorithm, so that the matching line matched with the GIS network database is completely matched, and the accurate display on a Gaode map is facilitated.
As a further solution, as shown in fig. 3, the picture recognition and classification unit is an AI-based picture recognition and classification unit, and trains the model through a disease picture classification training library to obtain characteristic values of disease classifications, which includes the following steps:
b1, collecting road disease pictures and carrying out classification treatment to obtain a disease picture classification training library;
b2, inputting the disease picture classification training library into a picture recognition and classification unit according to classification and extracting features;
b3, obtaining the characteristics of the road diseases and calculating the characteristic values to obtain the classification characteristic values of the diseases;
b4, adding the disease classification characteristic values corresponding to various road diseases into a disease classification characteristic value database.
As a further solution, the picture identification and classification unit obtains the classification result by:
c1, receiving and reporting road image data uploaded by the terminal;
c2 extracting road image data characteristics through an image identification and classification unit and calculating disease image characteristic values;
c3, writing the characteristic value of the disease picture into a Redis database for caching;
c4, writing the disease classification characteristic value database into a Redis database for caching;
c5, comparing and analyzing the characteristic value of the disease picture with the characteristic value database of the disease classification;
c6, if the disease picture characteristic value is matched with the disease classification characteristic value database, outputting a disease classification result; and if the matching is not established, the disease classification result is the unidentified disaster.
As a further solution, the GIS map visualization analysis system is a layered, dynamic and delivery map visualization system; the GIS map visualization analysis system comprises a GIS road network layer, a disease layer and a patrol layer; the GIS road network map layer comprises routes, road sections, bridges, tunnels, culverts, side slopes, intercommunication areas, traffic implementation, traffic volume and data files; the disease layer shows specific positions by a disease chart, wherein the specific positions comprise a disease audit state, a disease disposal state, a disease site, disease classification, a reporting unit, a disease picture, a disease area and disease volume information; the patrol map layer is used for displaying daily patrol real-time tracks of patrol personnel, and comprises patrol tracks, patrol times and patrol mileage.
As shown in fig. 4, in this embodiment, one-key reporting and inspection of a road disease is realized through a mobile phone APP, and users in villages, towns and counties use mobile terminals to report various road condition information such as road sealing, water accumulation, construction and the like. The road diseases are divided into district and county and country/village roads according to areas. The management area is planned with planned road diseases every year, and the diseases are planned as the diseases of a plan type according to the disease condition of the management area and the disease treatment budget cost. Diseases that are not planned and severe are classified as emergent. It can be divided into a plan class and an emergency class.
And (5) reporting the road disease. Firstly, if the disease is in a rural/village road, the patrolman begins to report the disease in the plan to a traffic management station, the traffic management station plans a disease treatment plan book, and a maintenance manager of a disease area begins to treat the disease through the approval of the station leader; if the disease is an unplanned disease, a traffic management station draws a disease treatment plan book, and the station leader approves the approval and needs to approve the approval, and then a maintenance manager of a disease area starts to treat the disease. Secondly, if the disease is a county road, the patrol personnel report plan diseases, a treatment scheme is drawn up by a department of the engineering stock, the maintenance section is long and is subjected to one-level examination, the first-level examination is passed, the department of the management department is established and is subjected to examination and passing, the examination and passing are ended, finally, the department of the engineering stock in the disease area is used for treating the disease process, and the department of the supervision department or the principal and the subordinate of a service center are required to carry out examination and recheck in the treatment of the county road. After the examination is passed, the disease treatment is completed.
In conclusion, the GPS-based deviation correction and intelligent road disease reporting system realizes multi-element automatic identification such as basic diseases, various intersection installation and application identification and judgment. The application of the new rural highway technology is promoted, the intelligent application of the GPS deviation correction technology, the picture recognition and classification technology, the GIS map visualization technology and other technologies is realized, the scientific and technological management is realized, the rural highway management and maintenance work is enabled, the rural joy is promoted, the rural economic development is driven, and the good social benefit is produced.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A deviation rectifying and road disease intelligent reporting system based on a GPS is characterized by comprising a reporting terminal and an intelligent analysis cloud;
the reporting terminal is deployed on mobile and/or vehicle-mounted intelligent equipment and is in communication connection with the intelligent analysis cloud terminal through a communication network; the reporting terminal can collect/upload GPS positioning data and road image data in real time and receive/upload the data through a data packet and an intelligent analysis cloud;
the intelligent analysis cloud is provided with a picture identification and classification unit, a GPS (global positioning system) deviation correction algorithm unit, a GPS basic database, a GIS network database and a GIS map visual analysis system;
the image identification and classification unit is used for receiving the road image data uploaded by the report terminal and carrying out image identification and classification on the road image data to obtain a road disease identification result;
the GPS deviation correction algorithm unit is used for receiving the GPS positioning data uploaded by the reporting terminal, and carrying out GPS route positioning and positioning deviation correction on the GPS positioning data to obtain an accurate positioning coordinate;
the GPS basic database is used for storing basic data required by GPS positioning; the GIS network database is used for storing GIS network data; the GIS map visualization analysis system is used for visually displaying the spatial information map of the reported information of the reporting terminal.
2. The system for correcting deviation and intelligently reporting road diseases based on the GPS according to claim 1, wherein a mysql database is adopted as a storage database and a Redis database is adopted as a cache database for the GPS basic database and the GIS network database; the Redis database is deployed with Redis Geo and performs data processing through Redis message queue data.
3. The system as claimed in claim 1, wherein the GPS deviation correction algorithm unit obtains the offset constant coefficient of the positioning section by the following steps
：
S1, receiving the GPS positioning data uploaded by the report terminal;
s2 setting the latitude and longitude of the current location point as upload point P (X)0，Y0）；
S3 finding nearby positioning point A (X) through Redis Geo1，Y1) The positioning point A belongs to the route L;
s4 matches any value of two forward and backward points through the positioning point A as the positioning point B (X)2，Y2）；
S5 finds the vector AB (X) between the anchor A and the anchor B2-X1，Y2-Y1) Wherein, in the step (A),
；
s6, making a perpendicular line from the uploading point P to the L line, and setting the intersection point as a perpendicular point Q (X, Y);
s7 finds the upload point P and the vector PQ (X-X)0，Y-Y0) Wherein, in the step (A),
；
s8 is represented by vector PQ perpendicular to vector AB, i.e.: the mode of the projection of the vector PQ on the vector AB is 0, the dot product of the projection is 0, and the coordinate of a Q point is calculated; wherein the content of the first and second substances,
；
s9, judging whether the point Q is on the straight line L line, namely calculating the formula:
whether the result is true or not; if yes, marking the point Q as a deviation correcting point Q corresponding to the uploading point P;
s10 calculating the distance S between the positioning point A and the deviation correcting point Q1Wherein, in the step (A),
；
s11, acquiring longitude and latitude of each point;
s12 calculates the actual distance S between locating point A longitude and latitude lat1, long 1 and uploading point P longitude and latitude lat2, long 22The calculation formula is as follows:
s13 reaction of S1And S2An offset is introduced, and an offset M of the upload point P is calculated, where M = S1/S2；
S14 repeating S1-S13 to obtain average value of multiple offsets of L line as regular offset constant coefficient
The formula is as follows:
(ii) a Wherein n is the measurement times, i is the measurement number, and the obtained offset constant coefficient
。
4. The system as claimed in claim 3, wherein the GPS deviation correction algorithm unit determines whether the GPS positioning data deviates from the GIS network data by:
a1 making a vertical line from the uploading point P to the vector AB to obtain a vertical point Q;
a2 judges whether the vertical point Q is on the segment AB: i.e. formula of calculation
Whether the result is true or not; if yes, the vertical point Q is in the AB line segment; if the vertical point Q is not in the AB line segment, the vertical point Q is not in the AB line segment;
a3 using offset constant coefficient if vertical point Q is not in AB line segment
Calculating a coordinate point of the vertical point Q in the AB line segment; if the vertical point Q is in the AB line segment, directly calculating the coordinate of the vertical point Q, and executing the next step;
a4, searching whether the coordinate corresponding to the vertical point Q exists in a GPS basic database, wherein a GPS repetition threshold is preset, and if the distance between the two points is greater than the GPS repetition threshold, determining the two different coordinates; if the distance between the two points is not greater than the GPS repetition threshold, the two points are regarded as two identical coordinates;
a5, if the coordinate corresponding to the vertical point Q already exists in the GPS basic database, only updating the reported information corresponding to the coordinate; and if the coordinate corresponding to the vertical point Q does not exist in the GPS basic database, newly building and storing the coordinate into the GPS basic database, and newly building the report information corresponding to the coordinate.
5. The system for correcting deviation and intelligently reporting road diseases based on GPS according to claim 1, wherein the picture recognition and classification unit is an AI-based picture recognition and classification unit, and the model is trained through a disease picture classification training library to obtain the characteristic values of each disease classification, comprising the following steps:
b1, collecting road disease pictures and carrying out classification treatment to obtain a disease picture classification training library;
b2, inputting the disease picture classification training library into a picture recognition and classification unit according to classification and extracting features;
b3, obtaining the characteristics of the road diseases and calculating the characteristic values to obtain the classification characteristic values of the diseases;
b4, adding the disease classification characteristic values corresponding to various road diseases into a disease classification characteristic value database.
6. The system for correcting deviation and intelligently reporting road diseases based on GPS according to claim 5, wherein the picture recognition and classification unit obtains the classification result by the following steps:
c1, receiving and reporting road image data uploaded by the terminal;
c2 extracting road image data characteristics through an image identification and classification unit and calculating disease image characteristic values;
c3, writing the characteristic value of the disease picture into a Redis database for caching;
c4, writing the disease classification characteristic value database into a Redis database for caching;
c5, comparing and analyzing the characteristic value of the disease picture with the characteristic value database of the disease classification;
c6, if the disease picture characteristic value is matched with the disease classification characteristic value database, outputting a disease classification result; and if the matching is not established, the disease classification result is the unidentified disaster.
7. The system for correcting deviation and intelligently reporting road diseases based on the GPS according to claim 1, wherein the GIS map visualization analysis system is a layered, dynamic and delivery map visualization system; the GIS map visualization analysis system comprises a GIS road network layer, a disease layer and a patrol layer; the GIS road network map layer comprises routes, road sections, bridges, tunnels, culverts, side slopes, intercommunication areas, traffic implementation, traffic volume and data files; the disease layer shows specific positions by a disease chart, wherein the specific positions comprise a disease audit state, a disease disposal state, a disease site, disease classification, a reporting unit, a disease picture, a disease area and disease volume information; the patrol map layer is used for displaying daily patrol real-time tracks of patrol personnel, and comprises patrol tracks, patrol times and patrol mileage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210195620.XA CN114282033B (en) | 2022-03-02 | 2022-03-02 | Deviation correction and intelligent road disease reporting system based on GPS |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210195620.XA CN114282033B (en) | 2022-03-02 | 2022-03-02 | Deviation correction and intelligent road disease reporting system based on GPS |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114282033A true CN114282033A (en) | 2022-04-05 |
| CN114282033B CN114282033B (en) | 2022-05-27 |
Family
ID=80882172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210195620.XA Active CN114282033B (en) | 2022-03-02 | 2022-03-02 | Deviation correction and intelligent road disease reporting system based on GPS |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114282033B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115630056A (en) * | 2022-10-20 | 2023-01-20 | 中远海运科技股份有限公司 | Road patrol quantitative assessment system and method based on GPS track positioning |
| CN116095099A (en) * | 2023-01-20 | 2023-05-09 | 广东省中山市质量计量监督检测所 | Machine vision-based mechanical part quality inspection system |
| CN116503027A (en) * | 2023-06-27 | 2023-07-28 | 成都智达万应科技有限公司 | Intelligent management system for highway assets |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007087163A (en) * | 2005-09-22 | 2007-04-05 | Mitsubishi Electric Corp | Coordinate correction method for map and apparatus |
| CN1996195A (en) * | 2005-12-31 | 2007-07-11 | 清华大学 | Locomotive positioning and rectifying system and its motion tracking method |
| CN101226406A (en) * | 2008-01-15 | 2008-07-23 | 郭卫平 | Sun tracking system and implementing method based on GPS and GIS |
| CN101794316A (en) * | 2010-03-30 | 2010-08-04 | 高翔 | Real-scene status consulting system and coordinate offset method based on GPS location and direction identification |
| CN102175254A (en) * | 2011-01-13 | 2011-09-07 | 北京超图软件股份有限公司 | Navigation positioning correction method and device, and positioning navigation system |
| CN103034737A (en) * | 2012-12-27 | 2013-04-10 | 天津市通卡公用网络***有限公司 | Deviation rectifying method of electronic map |
| CN104120645A (en) * | 2014-06-20 | 2014-10-29 | 南京理工技术转移中心有限公司 | Automatic test data positioning system and method for pavement condition |
| CN107357894A (en) * | 2017-07-13 | 2017-11-17 | 杭州智诚惠通科技有限公司 | A kind of road traffic facility data acquisition method for correcting error and system |
| CN107545744A (en) * | 2017-09-11 | 2018-01-05 | 公安部交通管理科学研究所 | Special duty's traffic signal control system and method based on GPS and the correction of GIS line matchings |
| CN108037723A (en) * | 2018-01-24 | 2018-05-15 | 武汉浩宇天辰科技有限公司 | A kind of highway maintenance Information Management System |
| CN108242149A (en) * | 2018-03-16 | 2018-07-03 | 成都智达万应科技有限公司 | A kind of big data analysis method based on traffic data |
| CN108759823A (en) * | 2018-05-28 | 2018-11-06 | 浙江大学 | The positioning of low speed automatic driving vehicle and method for correcting error in particular link based on images match |
| CN109827582A (en) * | 2019-03-29 | 2019-05-31 | 深圳市鹏途交通科技有限公司 | A kind of method and system of quick determining road network disease relative position |
| CN113064187A (en) * | 2021-04-07 | 2021-07-02 | 多彩贵州印象网络传媒股份有限公司 | Original GPS data-based longitude and latitude deviation rectifying capability algorithm |
| CN113253319A (en) * | 2021-04-29 | 2021-08-13 | 汉纳森（厦门）数据股份有限公司 | Road network extraction and track deviation correction method and system based on vehicle GPS |
| CN113269751A (en) * | 2021-05-27 | 2021-08-17 | 山东大学 | Pavement disease identification and repair method, system, equipment and medium based on convolutional neural network and 3D printing repair technology |
-
2022
- 2022-03-02 CN CN202210195620.XA patent/CN114282033B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007087163A (en) * | 2005-09-22 | 2007-04-05 | Mitsubishi Electric Corp | Coordinate correction method for map and apparatus |
| CN1996195A (en) * | 2005-12-31 | 2007-07-11 | 清华大学 | Locomotive positioning and rectifying system and its motion tracking method |
| CN101226406A (en) * | 2008-01-15 | 2008-07-23 | 郭卫平 | Sun tracking system and implementing method based on GPS and GIS |
| CN101794316A (en) * | 2010-03-30 | 2010-08-04 | 高翔 | Real-scene status consulting system and coordinate offset method based on GPS location and direction identification |
| CN102175254A (en) * | 2011-01-13 | 2011-09-07 | 北京超图软件股份有限公司 | Navigation positioning correction method and device, and positioning navigation system |
| CN103034737A (en) * | 2012-12-27 | 2013-04-10 | 天津市通卡公用网络***有限公司 | Deviation rectifying method of electronic map |
| CN104120645A (en) * | 2014-06-20 | 2014-10-29 | 南京理工技术转移中心有限公司 | Automatic test data positioning system and method for pavement condition |
| CN107357894A (en) * | 2017-07-13 | 2017-11-17 | 杭州智诚惠通科技有限公司 | A kind of road traffic facility data acquisition method for correcting error and system |
| CN107545744A (en) * | 2017-09-11 | 2018-01-05 | 公安部交通管理科学研究所 | Special duty's traffic signal control system and method based on GPS and the correction of GIS line matchings |
| CN108037723A (en) * | 2018-01-24 | 2018-05-15 | 武汉浩宇天辰科技有限公司 | A kind of highway maintenance Information Management System |
| CN108242149A (en) * | 2018-03-16 | 2018-07-03 | 成都智达万应科技有限公司 | A kind of big data analysis method based on traffic data |
| CN108759823A (en) * | 2018-05-28 | 2018-11-06 | 浙江大学 | The positioning of low speed automatic driving vehicle and method for correcting error in particular link based on images match |
| CN109827582A (en) * | 2019-03-29 | 2019-05-31 | 深圳市鹏途交通科技有限公司 | A kind of method and system of quick determining road network disease relative position |
| CN113064187A (en) * | 2021-04-07 | 2021-07-02 | 多彩贵州印象网络传媒股份有限公司 | Original GPS data-based longitude and latitude deviation rectifying capability algorithm |
| CN113253319A (en) * | 2021-04-29 | 2021-08-13 | 汉纳森（厦门）数据股份有限公司 | Road network extraction and track deviation correction method and system based on vehicle GPS |
| CN113269751A (en) * | 2021-05-27 | 2021-08-17 | 山东大学 | Pavement disease identification and repair method, system, equipment and medium based on convolutional neural network and 3D printing repair technology |
Non-Patent Citations (6)
| Title |
|---|
| 杨志鹏 等: "基于GPS/北斗车载终端回传位置信息的电子地图纠偏算法", 《计算机光盘软件与应用》 * |
| 杨志鹏 等: "基于GPS/北斗车载终端回传位置信息的电子地图纠偏算法", 《计算机光盘软件与应用》, vol. 17, no. 9, 31 May 2014 (2014-05-31), pages 17 - 19 * |
| 杨皎皎 等: "车载导航***中定位点纠偏方法的设计及实现", 《信息通信》 * |
| 杨皎皎 等: "车载导航***中定位点纠偏方法的设计及实现", 《信息通信》, no. 4, 15 June 2013 (2013-06-15), pages 43 - 44 * |
| 邱林 等: "利用中心点取值及常系数纠偏技术的电网GIS设备定位研究", 《电子世界》 * |
| 邱林 等: "利用中心点取值及常系数纠偏技术的电网GIS设备定位研究", 《电子世界》, no. 15, 31 August 2016 (2016-08-31), pages 159 - 160 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115630056A (en) * | 2022-10-20 | 2023-01-20 | 中远海运科技股份有限公司 | Road patrol quantitative assessment system and method based on GPS track positioning |
| CN115630056B (en) * | 2022-10-20 | 2024-01-26 | 中远海运科技股份有限公司 | Road inspection quantitative assessment system and method based on GPS track positioning |
| CN116095099A (en) * | 2023-01-20 | 2023-05-09 | 广东省中山市质量计量监督检测所 | Machine vision-based mechanical part quality inspection system |
| CN116503027A (en) * | 2023-06-27 | 2023-07-28 | 成都智达万应科技有限公司 | Intelligent management system for highway assets |
| CN116503027B (en) * | 2023-06-27 | 2024-01-19 | 成都智达万应科技有限公司 | Intelligent management system for highway assets |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114282033B (en) | 2022-05-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114282033B (en) | Deviation correction and intelligent road disease reporting system based on GPS | |
| CN105608505B (en) | Resident rail transit trip mode identification method based on mobile phone signaling data | |
| AU2006304589B2 (en) | System and method for identifying road features | |
| CN100580735C (en) | Real-time dynamic traffic information processing method based on car detecting technique | |
| CN111210612B (en) | Method for extracting bus route track based on bus GPS data and station information | |
| CN112580833B (en) | Rural highway maintenance informatization management system | |
| CN110222959B (en) | Urban employment reachability measuring and calculating method and system based on big data | |
| CN114037384A (en) | Logistics vehicle monitoring method, device, equipment and storage medium | |
| CN113531400B (en) | Pipeline line inspection method and system based on mobile equipment positioning data | |
| CN112036757A (en) | Parking transfer parking lot site selection method based on mobile phone signaling and floating car data | |
| CN112530158B (en) | Road network supplementing method based on historical track | |
| CN109147323A (en) | A kind of vehicle GPS data processing method for highway passenger and freight transportation indicator-specific statistics | |
| CN111829538A (en) | Traffic safety navigation method, storage medium and electronic equipment | |
| CN113360543B (en) | Method, device, equipment and storage medium for identifying repeated routes of public transportation | |
| Liao | Generating Reliable Freight Performance Measures with Truck GPS Data: Case Study in Twin Cities Metropolitan Area, Minnesota | |
| Flaskou et al. | Analysis of freight corridors using GPS data on trucks | |
| CN110160537A (en) | Detection method, system and the computer readable storage medium of vehicle operation | |
| CN112150650A (en) | Highway mileage charging management cloud platform based on big dipper high accuracy | |
| CN113706875B (en) | Road function studying and judging method | |
| CN115995151B (en) | Network vehicle-closing abnormal behavior detection method applied to city management | |
| CN115129796A (en) | Positioning data analysis method, apparatus, device and medium | |
| CN115630056B (en) | Road inspection quantitative assessment system and method based on GPS track positioning | |
| CN116659528A (en) | Road network grade pavement technical condition detection path planning method | |
| CN110599768A (en) | Highway traffic flow estimation method based on telecommunication big data | |
| CN113420439B (en) | Comprehensive traffic bearing capacity calculation method, device, computer equipment and storage medium |
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 | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A GPS based deviation correction and intelligent reporting system for highway diseases Effective date of registration: 20230411 Granted publication date: 20220527 Pledgee: Industrial Bank Limited by Share Ltd. Chengdu branch Pledgor: CHENGDU INTELLIGENT & OMNIPOTENT TECHNOLOGY CO.,LTD. Registration number: Y2023510000098 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Granted publication date: 20220527 Pledgee: Industrial Bank Limited by Share Ltd. Chengdu branch Pledgor: CHENGDU INTELLIGENT & OMNIPOTENT TECHNOLOGY CO.,LTD. Registration number: Y2023510000098 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A GPS based correction and intelligent reporting system for highway diseases Granted publication date: 20220527 Pledgee: Industrial Bank Limited by Share Ltd. Chengdu branch Pledgor: CHENGDU INTELLIGENT & OMNIPOTENT TECHNOLOGY CO.,LTD. Registration number: Y2024980027117 |