A flight zone pavement data analysis system based on intelligent patrol vehicles

Abstract

The present invention discloses a flight zone pavement data analysis system based on an intelligent patrol vehicle, a central server, a modeling module, a grid coordinate, a patrol vehicle, a radar scanning module, a density detection module, a level detection module, an abnormality module, an image scanning module, a wireless transmission, a weathering detection module, a void detection module, a texture detection, a water accumulation measurement module, an ice and snow layer detection module, a FOD identification, and a history recording module; the central server is used to receive, transmit, record, and organize pavement detection data. The flight zone pavement data analysis system based on the intelligent patrol vehicle establishes a 3D model of the airport pavement, and presents different problems and degrees of harm through different colors, so that the health status and changes of the airport pavement are more intuitive, which is convenient for monitoring by staff or intelligent systems, and can more reasonably arrange and adjust the take-off and landing of aircraft and pavement maintenance work.

Description

Technical Field

[0001] The present invention relates to the technical field of flight zone pavement monitoring, and in particular to a flight zone pavement data analysis system based on an intelligent patrol vehicle. Background Art

[0002] With the development of the economy, air transportation plays an increasingly important role in daily life. The number of newly added and expanded airports is gradually increasing. The increase in the number of airport flights has made the runway work at large and busy airports more arduous. Therefore, it is particularly important to take into account both flight punctuality and aircraft operation safety. In order to ensure the safety of aircraft takeoff and landing, the airport flight surface needs to be continuously detected to check for dangers.

[0003] Since the airport flight zone pavement area is very large, manual inspections are prone to omissions. At the same time, the data collected by detection is also very large. The relevant data recorded by manual inspections have the disadvantages of strong subjectivity, low accuracy and limited inspection range. The data cannot be analyzed accurately and quickly, and the problems cannot be detected in time. Therefore, we proposed an airfield pavement data analysis system based on intelligent patrol vehicles to solve the problem. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the object of the present invention is to provide an airfield pavement data analysis system based on an intelligent patrol vehicle, which has the function of visually and clearly observing the health status of the airfield pavement.

[0005] The present invention adopts the following technical solutions to achieve the technical purpose: a flight area pavement data analysis system based on an intelligent patrol vehicle, a central server, a modeling module, a grid coordinate, a patrol vehicle, a positioning module, a radar scanning module, a level detection module, an image scanning module, wireless transmission, air gap detection, texture detection, water accumulation measurement, ice and snow layer detection, FOD identification, and a history record module;

[0006] Central server, used to receive, record, and organize road surface inspection data and control patrol vehicles;

[0007] Modeling module, using automatic modeling software to build a proportional 3D model of the airport pavement, and at the same time present different colors according to the health status of each position of the pavement, making the pavement status more intuitive;

[0008] Grid coordinates are used to divide the established airport pavement proportional model into grid areas, and divide the grid areas according to the pavement planning information to facilitate the location of problem points and route planning for patrol vehicles;

[0009] The patrol vehicle is used to automatically drive according to the patrol route planned by grid coordinates, detect road surface information, and feed back the information to improve the established road surface model;

[0010] The positioning module is used to locate patrol vehicles so that the uploaded information can find the corresponding location according to the grid coordinates;

[0011] Radar scanning module, used to scan the health of the road surface using electromagnetic waves or lasers, find and locate problem points;

[0012] Image scanning module, used to take photos of the road surface conditions. For rigid road surfaces, each road panel on the inspection route is photographed and covered. For flexible road surfaces, the road surface on the inspection route is photographed and covered continuously. Each photo covers a sufficient area to ensure that no road surface defects in the inspection area are missed.

[0013] Infrared scanning is used to assist the image scanning module in identifying road surface FOD;

[0014] Wireless transmission is used to feed back the information detected by the radar scanning module and the image scanning module to the central server. After sorting and analyzing, the pavement model is improved through these data, and the information of air gaps, undulations, textures, rubber accumulation, water accumulation, ice and snow, and FOD is displayed on the model;

[0015] Hollowness detection, used to detect the hollowness of the pavement;

[0016] Level detection, used to detect the flatness of the road surface;

[0017] Texture detection, used to detect the width, depth, length and direction of road surface texture;

[0018] Glue accumulation detection, used to detect the glue accumulation on the road surface;

[0019] Water accumulation measurement, used to detect the depth of water accumulation on the road surface;

[0020] Ice and snow layer detection, used to detect the thickness of ice and snow on the pavement and the obstruction of the runway;

[0021] FOD recognition, used to detect FOD and its type on the pavement;

[0022] The history record module is used to record the problems that have occurred on the pavement, repairs and maintenance, and cleaning and protection conditions.

[0023] A method for operating an airfield pavement data analysis system based on an intelligent patrol vehicle, the steps comprising:

[0024] S1. Import the airport pavement construction information into the central server, and then use the automatic modeling software to build a 3D model of the pavement based on the construction information;

[0025] S2, dividing the established road surface model into grids, and defining the grids of each area according to the road surface planning;

[0026] S3. The patrol vehicle is positioned according to the grid coordinates and positioning module, and then plans the patrol route according to the flight information and special vehicle operation information, and drives at a constant speed along the patrol route. During the patrol process, the road surface conditions are recorded by radar scanning and image scanning;

[0027] S4. During the inspection, the electromagnetic waves of the ground penetrating radar are used to detect the depth and range of the road surface voids, and the laser radar is used to detect the road surface undulation based on the horizontal line, the width, depth, length and direction of the road surface texture, and the data is uploaded to the central server through wireless transmission equipment;

[0028] S5. During the inspection, the depth of rubber accumulation, water accumulation, thickness of ice and snow layer and runway coverage, FOD identification are detected through image scanning and infrared rays, and the data are uploaded to the central server through wireless transmission equipment;

[0029] S6. Arrange the information uploaded by the above modules to the central server, improve the road surface model according to the corresponding grid coordinate position, and present different colors on the grid coordinate according to the data content;

[0030] S7. The health of the road surface can be intuitively seen based on the color. If flight planning and maintenance arrangements allow, maintenance, cleaning and protection should be carried out as soon as possible, and relevant information should be recorded through the history record module;

[0031] S8. The central server analyzes the types and frequencies of problems in the grid coordinates based on the information recorded by the history record module, obtains the causes and influencing factors of the problems, facilitates later protection, and gives corresponding disease development predictions and maintenance suggestions;

[0032] S9. According to the types and severity of the above-mentioned diseases, the PCI and SCI indicators are calculated, and specific values ​​are given for each region. According to the relevant functional attenuation law, the service life of the airport runway based on the pavement surface function is given;

[0033] S10. Match and store daily weather information with patrol information, and access historical data in the pavement system at any time. Perform trend analysis, comparative analysis, and environmental impact analysis on historical data. The analysis results are presented in the form of visual charts for easy reference by the airport pavement management department.

[0034] S11. Based on the surface function status assessment and historical data analysis results of the airport pavement, remind the pavement maintenance precautions in the short, medium and long term, and give overall maintenance decision-making suggestions for the current status of the pavement;

[0035] S12. Quantify the health of the pavement presented in the S6 pavement model: a completely healthy pavement has a value of 100, and if health problems are found, the value is gradually reduced according to the severity, and the pavement that affects the operation of aircraft or special vehicles has a value of 60;

[0036] S13. Compare the pavement health values ​​before and after the aircraft takes off, and create a line chart with the data collected within 24 hours to observe the degree of pavement damage caused by aircraft of different models and loads;

[0037] When the pavement health value cannot support the operating losses of all flights in the next 48 hours based on flight information, an emergency repair warning will be issued.

[0038] As an optimization, the pavement texture depth data collected from the pavement is processed in S4, and the corresponding specifications are back-calculated into the indicators given in the specifications to evaluate the pavement texture depth. This back-calculation function needs to be studied. After the collection of the pavement texture depth values ​​is realized, it is necessary to determine the quantitative grading back-calculation relationship between the collected values ​​and the values ​​measured by the standard method through experimental research, and calculate the pavement texture depth evaluation index based on the back-calculation relationship to evaluate the pavement condition.

[0039] As an optimization, in S5, the overall rubber state of the road surface is evaluated based on the rubber accumulation area and the rubber accumulation depth, and rubber removal suggestions are given. When evaluating the rubber accumulation state, attention should be paid to distinguishing the pictures of the rubber accumulation area before and after rubber removal, and distinguishing the colors of the rubber accumulation area. The rubber removal suggestions include giving the rubber removal time, plan, expected effect goals, etc. based on the choice of rubber removal method and the average rubber removal speed.

[0040] As an optimization, the FOD recognition is connected to the Internet, and the scanned objects can be quickly and accurately identified through big data.

[0041] As an optimization, the area colors in S8 are divided into: the healthy area is grayish white, the hollow area is divided into 6 color segments according to size from grayish black to black, the road sinking area is divided into 6 color segments according to the drop from light cyan to green, the texture detection is divided into 6 color segments according to size from light brown to dark brown, the waterlogged area is divided into 6 color segments according to different depths from light blue to dark blue, the ice and snow area is divided into 6 color segments according to different depths from transparent to white, and the first three segments of the 6 color segments are safety lines, and the last three segments are danger lines.

[0042] As an optimization, the grid partitioning method in S2 is to first divide the road surface into large squares of 10m×10m according to the road surface size, then divide each large square into small squares of 1m×1m, and then divide each small square into fine grids of 1dm×1dm.

[0043] As an optimization, the cruise route in S3 may be temporarily adjusted or emergency avoided according to the actual operation of flights and special vehicles.

[0044] As an optimization, the quantified value of S12 is stored in the system background and does not need to be displayed on the model.

[0045] The present invention has the following beneficial effects:

[0046] 1. The airfield pavement data analysis system based on the intelligent patrol vehicle establishes a 3D model of the airport pavement and presents different problems and degrees of harm in different colors, making the health status and changes of the airport pavement more intuitive, convenient for monitoring by staff or intelligent systems, and more reasonably arranging and adjusting aircraft take-offs and landings and pavement maintenance work.

[0047] 2. The flight zone pavement data analysis system based on the intelligent patrol vehicle can locate the problem points more accurately by dividing the area into grids, which provides convenience for the later problem investigation and repair, so that the uploaded information can find the corresponding position in the grid coordinates. Through the accuracy of the model, the grid partition also provides route guidance for patrol vehicles, focuses on the detection of problem-prone points, and can also accurately adjust the route according to the real-time information of the flight or pavement.

[0048] 3. The flight zone pavement data analysis system based on the intelligent patrol vehicle analyzes the information recorded in the history record module, determines the type and frequency of problems in a single grid area, the causes and influencing factors of the problems, and can make targeted protection and maintenance suggestions based on the results. Through big data analysis, the development of diseases can be predicted, which reduces the subsequent maintenance costs, improves pavement safety, and is more conducive to standardized management of pavements.

[0049] 4. The airfield pavement data analysis system based on the intelligent patrol vehicle quantifies the pavement health presented in the S6 pavement model, compares the pavement health values ​​before and after the aircraft takes off, and establishes a line chart of the data collected within 24 hours. It then calculates based on flight information. When the pavement health value cannot support the operating losses of all flights in the next 48 hours, an emergency repair warning is issued. This estimate of the pavement health condition has passed the safety test. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow module diagram of the present invention.

[0051] Figure 2 This is a detection module diagram of the present invention.

[0052] Figure 3 This is a schematic diagram of modeling simulation of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] See also Figure 1-2 , a flight zone pavement data analysis system based on an intelligent patrol vehicle, a central server, a modeling module, a grid coordinate, a patrol vehicle, a positioning module, a radar scanning module, a level detection module, an image scanning module, wireless transmission, air gap detection, texture detection, water accumulation measurement, ice and snow layer detection, FOD identification, and a history record module;

[0056] Central server, used to receive, record, and organize road surface inspection data and control patrol vehicles;

[0057] Modeling module, using automatic modeling software to build a proportional 3D model of the airport pavement, and at the same time present different colors according to the health status of each position of the pavement, making the pavement status more intuitive;

[0058] Grid coordinates are used to divide the established airport pavement proportional model into grid areas, and divide the grid areas according to the pavement planning information to facilitate the location of problem points and route planning for patrol vehicles;

[0059] The patrol vehicle is used to automatically drive according to the patrol route planned by grid coordinates, detect road surface information, and feed back the information to improve the established road surface model;

[0060] The positioning module is used to locate patrol vehicles so that the uploaded information can find the corresponding location according to the grid coordinates;

[0061] Radar scanning module, used to scan the health of the road surface using electromagnetic waves or lasers, find and locate problem points;

[0062] Image scanning module, used to take photos of the road surface conditions. For rigid road surfaces, each road panel on the inspection route is photographed and covered. For flexible road surfaces, the road surface on the inspection route is photographed and covered continuously. Each photo covers a sufficient area to ensure that no road surface defects in the inspection area are missed.

[0063] Infrared scanning is used to assist the image scanning module in identifying road surface FOD;

[0064] Wireless transmission is used to feed back the information detected by the radar scanning module and the image scanning module to the central server. After sorting and analyzing, the pavement model is improved through these data, and the information of air gaps, undulations, textures, rubber accumulation, water accumulation, ice and snow, and FOD is displayed on the model;

[0065] Hollowness detection, used to detect the hollowness of the pavement;

[0066] Level detection, used to detect the flatness of the road surface;

[0067] Texture detection, used to detect the width, depth, length and direction of road surface texture;

[0068] Glue accumulation detection, used to detect the glue accumulation on the road surface;

[0069] Water accumulation measurement, used to detect the depth of water accumulation on the road surface;

[0070] Ice and snow layer detection, used to detect the thickness of ice and snow on the pavement and the obstruction of the runway;

[0071] FOD recognition, used to detect FOD and its type on the pavement;

[0072] The history record module is used to record the problems that have occurred on the pavement, repairs and maintenance, and cleaning and protection conditions.

[0073] A method for operating an airfield pavement data analysis system based on an intelligent patrol vehicle, the steps comprising:

[0074] S1. Import the airport pavement construction information into the central server, and then use the automatic modeling software to build a 3D model of the pavement based on the construction information;

[0075] S2, dividing the established road surface model into grids, and defining the grids of each area according to the road surface planning;

[0076] S3. The patrol vehicle is positioned according to the grid coordinates and positioning module, and then plans the patrol route according to the flight information and special vehicle operation information, and drives at a constant speed along the patrol route. During the patrol process, the road surface conditions are recorded by radar scanning and image scanning;

[0077] S4. During the inspection, electromagnetic waves are used to detect the depth and range of the road surface voids, and lasers are used to detect the road surface undulation based on the horizontal line, the width, depth, length and direction of the road surface texture, and the data is uploaded to the central server through wireless transmission equipment;

[0078] S5. During the inspection, the depth of rubber accumulation, water accumulation, thickness of ice and snow layer and runway coverage, FOD identification are detected through image scanning and infrared rays, and the data are uploaded to the central server through wireless transmission equipment;

[0079] S6. Arrange the information uploaded by the above modules to the central server, improve the road surface model according to the corresponding grid coordinate position, and present different colors on the grid coordinate according to the data content;

[0080] S7. The health of the road surface can be intuitively seen by color. When flight planning and maintenance arrangements allow, maintenance, cleaning and protection can be carried out as soon as possible, and relevant information can be recorded through the history record module.

[0081] information;

[0082] FOD recognition is connected to the Internet and can quickly and accurately identify scanned objects through big data.

[0083] The area colors in S8 are divided into: healthy area is grayish white, the void area is divided into 6 color segments from grayish black to black according to size, the road subsidence area is divided into 6 color segments from light blue to green according to the drop, the texture detection is divided into 6 color segments from light brown to dark brown according to size, the waterlogged area is divided into 6 color segments from light blue to dark blue according to different depths, the ice and snow area is divided into 6 color segments from transparent to white according to different depths, and the first three segments of the 6 color segments are safety lines, and the last three segments are danger lines.

[0084] The grid partitioning method in S2 is to first divide the road surface into large squares of 10m×10m according to the size of the road surface, then divide each large square into small squares of 1m×1m, and then divide each small square into fine grids of 1dm×1dm.

[0085] The cruise route in S3 will be temporarily adjusted or emergency avoided according to the actual operation of flights and special vehicles.

[0086] By establishing a 3D model of the airport pavement and presenting different problems and degrees of hazard through different colors, the health status and changes of the airport pavement can be made more intuitive, which is convenient for monitoring by staff or intelligent systems, and can more reasonably arrange and adjust aircraft takeoffs and landings and pavement maintenance work.

[0087] By dividing the area into grids, problem points can be located more accurately, which facilitates subsequent problem investigation and repair. At the same time, grid division also provides route guidance for patrol vehicles, focusing on inspections of problem-prone points. Routes can also be accurately adjusted based on real-time information about flights or pavement.

[0088] Example 2

[0089] See also Figure 1-2, a flight zone pavement data analysis system based on an intelligent patrol vehicle, a central server, a modeling module, a grid coordinate, a patrol vehicle, a positioning module, a radar scanning module, a level detection module, an image scanning module, wireless transmission, air gap detection, texture detection, water accumulation measurement, ice and snow layer detection, FOD identification, and a history record module;

[0090] Central server, used to receive, record, and organize road surface inspection data and control patrol vehicles;

[0091] Modeling module, using automatic modeling software to build a proportional 3D model of the airport pavement, and at the same time present different colors according to the health status of each position of the pavement, making the pavement status more intuitive;

[0092] Grid coordinates are used to divide the established airport pavement proportional model into grid areas, and divide the grid areas according to the pavement planning information to facilitate the location of problem points and route planning for patrol vehicles;

[0093] The patrol vehicle is used to automatically drive according to the patrol route planned by grid coordinates, detect road surface information, and feed back the information to improve the established road surface model;

[0094] The positioning module is used to locate patrol vehicles so that the uploaded information can find the corresponding location according to the grid coordinates;

[0095] Radar scanning module, used to scan the health of the road surface using electromagnetic waves or lasers, find and locate problem points;

[0096] Image scanning module, used to take photos of the road surface conditions. For rigid road surfaces, each road panel on the inspection route is photographed and covered. For flexible road surfaces, the road surface on the inspection route is photographed and covered continuously. Each photo covers a sufficient area to ensure that no road surface defects in the inspection area are missed.

[0097] Infrared scanning is used to assist the image scanning module in identifying road surface FOD;

[0098] Wireless transmission is used to feed back the information detected by the radar scanning module and the image scanning module to the central server. After sorting and analyzing, the pavement model is improved through these data, and the information of air gaps, undulations, textures, rubber accumulation, water accumulation, ice and snow, and FOD is displayed on the model;

[0099] Hollowness detection, used to detect the hollowness of the pavement;

[0100] Level detection, used to detect the flatness of the road surface;

[0101] Texture detection, used to detect the width, depth, length and direction of road surface texture;

[0102] Glue accumulation detection, used to detect the glue accumulation on the road surface;

[0103] Water accumulation measurement, used to detect the depth of water accumulation on the road surface;

[0104] Ice and snow layer detection, used to detect the thickness of ice and snow on the pavement and the obstruction of the runway;

[0105] FOD identification, used to detect the type of FOD on the pavement;

[0106] The history record module is used to record the problems that have occurred on the pavement, repairs and maintenance, and cleaning and protection conditions.

[0107] A method for operating an airfield pavement data analysis system based on an intelligent patrol vehicle, the steps comprising:

[0108] S1. Import the airport pavement construction information into the central server, and then use the automatic modeling software to build a 3D model of the pavement based on the construction information;

[0109] S2, dividing the established road surface model into grids, and defining the grids of each area according to the road surface planning;

[0110] S3. The patrol vehicle is positioned according to the grid coordinates and positioning module, and then plans the patrol route according to the flight information and special vehicle operation information, and drives at a constant speed along the patrol route. During the patrol process, the road surface conditions are recorded by radar scanning and image scanning;

[0111] S4. During the inspection, electromagnetic waves are used to detect the depth and range of the road surface voids, and lasers are used to detect the road surface undulation based on the horizontal line, the width, depth, length and direction of the road surface texture, and the data is uploaded to the central server through wireless transmission equipment;

[0112] S5. During the inspection, the depth of rubber accumulation, water accumulation, thickness of ice and snow layer and the cover of the runway are detected through image scanning and infrared rays, and FOD is identified, and the data is uploaded to the central server through wireless transmission equipment;

[0113] S6. Arrange the information uploaded by the above modules to the central server, improve the road surface model according to the corresponding grid coordinate position, and present different colors on the grid coordinate according to the data content;

[0114] S7. The health of the road surface can be intuitively seen based on the color. If flight planning and maintenance arrangements allow, maintenance, cleaning and protection should be carried out as soon as possible, and relevant information should be recorded through the history record module;

[0115] S8. The central server analyzes the types and frequencies of problems in the grid coordinates based on the information recorded by the history record module, obtains the causes and influencing factors of the problems, facilitates later protection, and gives corresponding disease development predictions and maintenance suggestions;

[0116] S9. According to the types and severity of the above-mentioned diseases, the PCI and SCI indicators are calculated, and specific values ​​are given for each region. According to the relevant functional attenuation law, the service life of the airport runway based on the pavement surface function is given;

[0117] S10. Match and store daily weather information with patrol information, and access historical data in the pavement system at any time. Perform trend analysis, comparative analysis, and environmental impact analysis on historical data. The analysis results are presented in the form of visual charts for easy reference by the airport pavement management department.

[0118] S11. Based on the surface function status assessment and historical data analysis results of the airport pavement, remind the pavement maintenance precautions in the short, medium and long term, and give overall maintenance decision-making suggestions for the current status of the pavement;

[0119] S12. Quantify the health of the pavement presented in the S6 pavement model: a completely healthy pavement has a value of 100, and if health problems are found, the value is gradually reduced according to the severity, and the pavement that affects the operation of aircraft or special vehicles has a value of 60;

[0120] S13. Compare the pavement health values ​​before and after the aircraft takes off, and create a line chart with the data collected within 24 hours to observe the degree of pavement damage caused by aircraft of different models and loads;

[0121] When the pavement health value cannot support the operating losses of all flights in the next 48 hours based on flight information, an emergency repair warning will be issued.

[0122] FOD recognition is connected to the Internet and can quickly and accurately identify scanned objects through big data.

[0123] By analyzing the information recorded in the history record module, the types and frequencies of problems in a single grid area can be judged, as well as the causes and influencing factors of the problems. Targeted protection can be carried out based on the results obtained, thus reducing subsequent maintenance costs, improving pavement safety, and being more conducive to standardized pavement management.

[0124] Example 3

[0125] See also Figure 1-3, a flight zone pavement data analysis system based on an intelligent patrol vehicle, a central server, a modeling module, a grid coordinate, a patrol vehicle, a positioning module, a radar scanning module, a level detection module, an image scanning module, wireless transmission, air gap detection, texture detection, water accumulation measurement, ice and snow layer detection, FOD identification, and a history record module;

[0126] Central server, used to receive, record, and organize road surface inspection data and control patrol vehicles;

[0127] Modeling module, using automatic modeling software to build a proportional 3D model of the airport pavement, and at the same time present different colors according to the health status of each position of the pavement, making the pavement status more intuitive;

[0128] Grid coordinates are used to divide the established airport pavement proportional model into grid areas, and divide the grid areas according to the pavement planning information to facilitate the location of problem points and route planning for patrol vehicles;

[0129] The patrol vehicle is used to automatically drive according to the patrol route planned by grid coordinates, detect road surface information, and feed back the information to improve the established road surface model;

[0130] The positioning module is used to locate patrol vehicles so that the uploaded information can find the corresponding location according to the grid coordinates;

[0131] Radar scanning module, used to scan the health of the road surface using electromagnetic waves or lasers, find and locate problem points;

[0132] Image scanning module, used to take photos of the road surface conditions. For rigid road surfaces, each road panel on the inspection route is photographed and covered. For flexible road surfaces, the road surface on the inspection route is photographed and covered continuously. Each photo covers a sufficient area to ensure that no road surface defects in the inspection area are missed.

[0133] Infrared scanning is used to assist the image scanning module in identifying road surface FOD;

[0134] Wireless transmission is used to feed back the information detected by the radar scanning module and the image scanning module to the central server. After sorting and analyzing, the pavement model is improved through these data, and the information of air gaps, undulations, textures, rubber accumulation, water accumulation, ice and snow, and FOD is displayed on the model;

[0135] Hollowness detection, used to detect the hollowness of the pavement;

[0136] Level detection, used to detect the flatness of the road surface;

[0137] Texture detection, used to detect the width, depth, length and direction of road surface texture;

[0138] Glue accumulation detection, used to detect the glue accumulation on the road surface;

[0139] Water accumulation measurement, used to detect the depth of water accumulation on the road surface;

[0140] Ice and snow layer detection, used to detect the thickness of ice and snow on the pavement and the obstruction of the runway;

[0141] FOD recognition, used to detect FOD and its type on the pavement;

[0142] The history record module is used to record the problems that have occurred on the pavement, repairs and maintenance, and cleaning and protection conditions.

[0143] A method for operating an airfield pavement data analysis system based on an intelligent patrol vehicle, the steps comprising:

[0144] S1. Import the airport pavement construction information into the central server, and then use the automatic modeling software to build a 3D model of the pavement based on the construction information;

[0145] S2, dividing the established road surface model into grids, and defining the grids of each area according to the road surface planning;

[0146] S3. The patrol vehicle is positioned according to the grid coordinates and positioning module, and then plans the patrol route according to the flight information and special vehicle operation information, and drives at a constant speed along the patrol route. During the patrol process, the road surface conditions are recorded by radar scanning and image scanning;

[0147] S4. During the inspection, electromagnetic waves are used to detect the depth and range of the road surface voids, and lasers are used to detect the road surface undulation based on the horizontal line, the width, depth, length and direction of the road surface texture, and the data is uploaded to the central server through wireless transmission equipment;

[0148] S5. During the inspection, the depth of rubber accumulation, water accumulation, thickness of ice and snow layer and the cover of the runway are detected through image scanning and infrared rays, and FOD is identified, and the data is uploaded to the central server through wireless transmission equipment;

[0149] S6. Arrange the information uploaded by the above modules to the central server, improve the road surface model according to the corresponding grid coordinate position, and present different colors on the grid coordinate according to the data content;

[0150] S7. The health of the road surface can be intuitively seen based on the color. If flight planning and maintenance arrangements allow, maintenance, cleaning and protection should be carried out as soon as possible, and relevant information should be recorded through the history record module;

[0151] S8. The central server analyzes the types and frequencies of problems in the grid coordinates based on the information recorded by the history record module, obtains the causes and influencing factors of the problems, facilitates later protection, and gives corresponding disease development predictions and maintenance suggestions;

[0152] S9. According to the types and severity of the above-mentioned diseases, the PCI and SCI indicators are calculated, and specific values ​​are given for each region. According to the relevant functional attenuation law, the service life of the airport runway based on the pavement surface function is given;

[0153] S10. Match and store daily weather information with patrol information, and access historical data in the pavement system at any time. Perform trend analysis, comparative analysis, and environmental impact analysis on historical data. The analysis results are presented in the form of visual charts for easy reference by the airport pavement management department.

[0154] S11. Based on the surface function status assessment and historical data analysis results of the airport pavement, remind the pavement maintenance precautions in the short, medium and long term, and give overall maintenance decision-making suggestions for the current status of the pavement;

[0155] S12. Quantify the health of the pavement presented in the S6 pavement model: a completely healthy pavement has a value of 100, and if health problems are found, the value is gradually reduced according to the severity, and the pavement that affects the operation of aircraft or special vehicles has a value of 60;

[0156] S13. Compare the pavement health values ​​before and after the aircraft takes off, and create a line chart with the data collected within 24 hours to observe the degree of pavement damage caused by aircraft of different models and loads;

[0157] When the pavement health value cannot support the operating losses of all flights in the next 48 hours based on flight information, an emergency repair warning will be issued.

[0158] As an optimization, the pavement texture depth data collected from the pavement is processed in S4, and the corresponding specifications are back-calculated into the indicators given in the specifications to evaluate the pavement texture depth. This back-calculation function needs to be studied. After the collection of the pavement texture depth values ​​is realized, it is necessary to determine the quantitative grading back-calculation relationship between the collected values ​​and the values ​​measured by the standard method through experimental research, and calculate the pavement texture depth evaluation index based on the back-calculation relationship to evaluate the pavement condition.

[0159] As an optimization, in S5, the overall rubber state of the road surface is evaluated based on the rubber accumulation area and the rubber accumulation depth, and rubber removal suggestions are given. When evaluating the rubber accumulation state, attention should be paid to distinguishing the pictures of the rubber accumulation area before and after rubber removal, and distinguishing the colors of the rubber accumulation area. The rubber removal suggestions include giving the rubber removal time, plan, expected effect goals, etc. based on the choice of rubber removal method and the average rubber removal speed.

[0160] As an optimization, the FOD recognition is connected to the Internet, and the scanned objects can be quickly and accurately identified through big data.

[0161] As an optimization, the area colors in S8 are divided into: the healthy area is grayish white, the hollow area is divided into 6 color segments according to size from grayish black to black, the road sinking area is divided into 6 color segments according to the drop from light cyan to green, the texture detection is divided into 6 color segments according to size from light brown to dark brown, the waterlogged area is divided into 6 color segments according to different depths from light blue to dark blue, the ice and snow area is divided into 6 color segments according to different depths from transparent to white, and the first three segments of the 6 color segments are safety lines, and the last three segments are danger lines.

[0162] As an optimization, the grid partitioning method in S2 is to first divide the road surface into large squares of 10m×10m according to the road surface size, then divide each large square into small squares of 1m×1m, and then divide each small square into fine grids of 1dm×1dm.

[0163] As an optimization, the cruise route in S3 may be temporarily adjusted or emergency avoided according to the actual operation of flights and special vehicles.

[0164] As an optimization, the quantized values ​​of S12 are stored in the system background and do not need to be displayed on the model. It can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the attached claims and their equivalents.

Claims

1. An airfield pavement data analysis system based on an intelligent patrol vehicle, characterized in that: Central server, modeling module, grid coordinates, patrol vehicles, positioning module, radar scanning module, level detection module, image scanning module, wireless transmission, air gap detection, texture detection, water accumulation measurement, ice and snow layer detection, FOD identification, historical record module; Central server, used to receive, record, and organize road surface inspection data and control patrol vehicles; Modeling module, using automatic modeling software to build a proportional 3D model of the airport pavement, and at the same time present different colors according to the health status of each position of the pavement, making the pavement status more intuitive; Grid coordinates are used to divide the established airport pavement proportional model into grid areas, and divide the grid areas according to the pavement planning information to facilitate the location of problem points and route planning for patrol vehicles; The patrol vehicle is used to automatically drive according to the patrol route planned by grid coordinates, detect road surface information, and feed back the information to improve the established road surface model; Positioning module, used to locate patrol vehicles so that the uploaded information can find the corresponding location according to the grid coordinates; Radar scanning module, used to scan the health of the road surface using electromagnetic waves or lasers, find and locate problem points; Image scanning module, used to take photos of the road surface conditions. For rigid road surfaces, each road panel on the inspection route is photographed and covered. For flexible road surfaces, the road surface on the inspection route is photographed and covered continuously. Each photo covers a sufficient area to ensure that no road surface defects in the inspection area are missed. Infrared scanning is used to assist the image scanning module in identifying road surface FOD; Wireless transmission is used to feed back the information detected by the radar scanning module and the image scanning module to the central server. After sorting and analyzing, the pavement model is improved through these data, and the information of air gaps, undulations, textures, rubber accumulation, water accumulation, ice and snow, and FOD is displayed on the model; Hollowness detection, used to detect the hollowness of the pavement; Level detection, used to detect the flatness of the road surface; Texture detection, used to detect the width, depth, length and direction of road surface texture; Glue accumulation detection, used to detect the glue accumulation on the road surface; Water accumulation measurement, used to detect the depth of water accumulation on the road surface; Ice and snow layer detection, used to detect the thickness of ice and snow on the pavement and the obstruction of the runway; FOD recognition, used to detect FOD and its type on the pavement; The history record module is used to record the problems that have occurred on the pavement, repair and maintenance conditions, and cleaning and protection conditions.

2. A method for operating an airfield pavement data analysis system based on an intelligent patrol vehicle, characterized in that: include: S1. Import the airport pavement construction information into the central server, and then use the automatic modeling software to build a 3D model of the pavement based on the construction information; S2, dividing the established road surface model into grids, and defining the grids of each area according to the road surface planning; S3. The patrol vehicle is positioned according to the grid coordinates and positioning module, and then plans the patrol route according to the flight information and special vehicle operation information, and drives at a constant speed along the patrol route. During the patrol process, the road surface conditions are recorded by radar scanning and image scanning; S4. During the inspection, the electromagnetic waves of the ground penetrating radar are used to detect the depth and range of the road surface voids, and the laser radar is used to detect the road surface undulation based on the horizontal line, the width, depth, length and direction of the road surface texture, and the data is uploaded to the central server through wireless transmission equipment; S5. During the inspection, the depth of rubber accumulation, water accumulation, thickness of ice and snow layer and runway coverage, FOD identification are detected through image scanning and infrared rays, and the data are uploaded to the central server through wireless transmission equipment; S6. Arrange the information uploaded by the above modules to the central server, improve the road surface model according to the corresponding grid coordinate position, and present different colors on the grid coordinate according to the data content; S7. The health of the road surface can be intuitively seen based on the color. If flight planning and maintenance arrangements allow, maintenance, cleaning and protection should be carried out as soon as possible, and relevant information should be recorded through the history record module; S8. The central server analyzes the types and frequencies of problems in the grid coordinates based on the information recorded by the history record module, obtains the causes and influencing factors of the problems, facilitates later protection, and gives corresponding disease development predictions and maintenance suggestions; S9. According to the types and severity of the above-mentioned diseases, the PCI and SCI indicators are calculated, and specific values ​​are given for each region. According to the relevant functional attenuation law, the service life of the airport runway based on the pavement surface function is given; S10. Match and store daily weather information with patrol information, and access historical data in the pavement system at any time. Perform trend analysis, comparative analysis, and environmental impact analysis on historical data. The analysis results are presented in the form of visual charts for easy reference by the airport pavement management department. S11. Based on the surface function status assessment and historical data analysis results of the airport pavement, remind the pavement maintenance precautions in the short, medium and long term, and give overall maintenance decision-making suggestions for the current status of the pavement; S12. Quantify the health of the pavement presented in the S6 pavement model: a completely healthy pavement has a value of 100, and if health problems are found, the value is gradually reduced according to the severity, and the pavement that affects the operation of aircraft or special vehicles has a value of 60; S13. Compare the pavement health values ​​before and after takeoff, and create a line chart of the data collected within 24 hours to observe the degree of pavement damage caused by aircraft of different models and loads; When the pavement health value cannot support the operating losses of all flights in the next 48 hours based on flight information, an emergency repair warning will be issued.

3. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: In the S4, the pavement texture depth data collected from the pavement is processed and back-calculated into the index given in the specification according to the specification to evaluate the pavement texture depth. This back-calculation function needs to be studied. After the pavement texture depth value is collected, it is necessary to determine the quantitative grading back-calculation relationship between the collected value and the value measured by the specification method through experimental research, calculate the pavement texture depth evaluation index based on the back-calculation relationship, and evaluate the pavement condition.

4. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: In S5, the overall rubber state of the road surface is evaluated based on the rubber accumulation area and the rubber accumulation depth, and rubber removal suggestions are given. When evaluating the rubber accumulation state, attention should be paid to distinguishing the pictures of the rubber accumulation area before and after rubber removal, and distinguishing the colors of the rubber accumulation area. The rubber removal suggestions include giving the rubber removal time, plan, expected effect goals, etc. based on the choice of rubber removal method and the average rubber removal speed.

5. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: The FOD recognition is connected to the Internet and can quickly and accurately identify scanned objects through big data.

6. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: The area colors in S8 are divided into: the healthy area is grayish white, the hollow area is divided into 6 color segments from grayish black to black according to size, the road subsidence area is divided into 6 color segments from light blue to green according to the drop, the texture detection is divided into 6 color segments from light brown to dark brown according to size, the waterlogged area is divided into 6 color segments from light blue to dark blue according to different depths, the ice and snow area is divided into 6 color segments from transparent to white according to different depths, and the first three segments of the 6 color segments are safety lines, and the last three segments are danger lines.

7. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: The grid partitioning method in S2 is to first divide the road surface into large squares of 10m×10m according to the road surface size, then divide each large square into small squares of 1m×1m, and then divide each small square into fine grids of 1dm×1dm.

8. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: The cruise route in S3 may be temporarily adjusted or emergency avoided according to the actual operation of the flight and special vehicles.

9. The method for operating the flight zone pavement data analysis system based on the intelligent patrol vehicle according to claim 2 is characterized in that: The quantified value of S12 is stored in the system background and does not need to be displayed on the model.

Images