A road intelligent maintenance method, system and medium based on equivalent axle times

Abstract

The present application provides a method, system and medium for intelligent road maintenance based on equivalent axle times, which belongs to the field of road maintenance and big data technology. The method includes: obtaining road vehicle traffic information and road status information of a preset area within a preset time period, extracting road design parameter information and road disease detection information according to the road status information, querying according to the road vehicle traffic information, obtaining equivalent axle times data, and then processing to obtain cumulative equivalent axle times data, combining the road design parameter information to obtain a road tolerance index, obtaining a road damage index according to the road disease detection information, and comparing and processing with the road tolerance index to obtain a road damage tolerance matching index and determine whether the road damage is normal. The present application realizes the quantitative conversion from raw data to evaluation results. Based on professional knowledge and practical experience, it can accurately reflect the actual condition of the road and ensure the comprehensiveness and accuracy of the evaluation.

Description

Technical Field

[0001] The present application relates to the field of road maintenance and big data technology, and more specifically, to a method, system and medium for intelligent road maintenance based on equivalent axle times. Background Art

[0002] The development of background technology of traditional road maintenance is mainly based on the historical process of highway construction and use. With the continuous advancement of highway construction and the continuous increase in highway mileage, the demand for highway maintenance is also increasing. Traditional road maintenance technology mainly relies on manual operation and simple mechanical equipment, which played an important role in the early stage of highway maintenance.

[0003] However, with the continuous increase in traffic volume and the continuous changes in road use conditions, traditional road maintenance technology has gradually exposed problems such as low efficiency and difficulty in ensuring maintenance quality. Current road maintenance technology lacks a technical method that can quantitatively evaluate the degree of road damage based on equivalent axle times and can perform targeted maintenance based on the degree of damage to the road caused by different vehicles, thereby improving maintenance efficiency and effectiveness.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of the present application is to provide a road intelligent maintenance method, system and medium based on equivalent axle times, which can obtain road vehicle traffic information and road status information of a preset area within a preset time period, extract road design parameter information and road disease detection information according to the road status information, query according to the road vehicle traffic information, obtain equivalent axle times data, and then process to obtain cumulative equivalent axle times data, obtain a road tolerance index in combination with the road design parameter information, obtain a road damage index according to the road disease detection information, compare and process with the road tolerance index, obtain a road damage tolerance matching index and compare with a preset matching index threshold to determine whether the road damage is normal. The present application comprehensively considers road vehicle traffic information and road status information, realizes quantitative conversion from raw data to evaluation results, and based on professional knowledge and practical experience, can accurately reflect the actual conditions of the road and ensure the comprehensiveness and accuracy of the evaluation.

[0006] The present application provides a road intelligent maintenance method based on equivalent axle times, comprising the following steps:

[0007] Obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information based on the road status information;

[0008] According to the road vehicle traffic information, query through a preset equivalent database to obtain equivalent axle data;

[0009] Processing the equivalent axle frequency data through a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data;

[0010] The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model;

[0011] Processing the road damage detection information through a preset road damage degree model to obtain a road damage degree index;

[0012] A comparison process is performed based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index, and the index is compared with a preset matching index threshold to determine whether the road damage is normal.

[0013] Among them, in the road intelligent maintenance method based on equivalent axle times described in the present application, the equivalent axle times data is obtained by querying a preset equivalent database according to the road vehicle traffic information, specifically:

[0014] Extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information;

[0015] The equivalent axle number data is obtained by querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle total load data, axle number coefficient and wheel group coefficient.

[0016] Among them, in the road intelligent maintenance method based on equivalent axle count described in the present application, the equivalent axle count data is processed by a preset axle count statistical model to obtain the accumulated equivalent axle count data, specifically:

[0017] Obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period;

[0018] The equivalent axle-order data is combined with the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

[0019] Among them, in the road intelligent maintenance method based on equivalent axle times described in the present application, the road design parameter information is combined with the accumulated equivalent axle times data through a preset tolerance model to obtain a road tolerance index, which is specifically:

[0020] Extracting design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data, and road bearing capacity data according to the road design parameter information;

[0021] The road tolerance index is obtained by processing the designed speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data and road bearing capacity data in combination with the cumulative equivalent axle data through a preset tolerance model.

[0022] Among them, in the road intelligent maintenance method based on equivalent axle times described in the present application, the road damage index is obtained by processing the road disease detection information through a preset road damage model, specifically:

[0023] Extracting disease type characteristic data, disease degree data, disease total amount data and disease location distribution data according to the road disease detection information;

[0024] The road damage index is obtained by processing the disease type characteristic data, disease degree data, disease total amount data and disease location distribution data through a preset road damage degree model.

[0025] Among them, in the road intelligent maintenance method based on equivalent axle times described in the present application, the road damage tolerance matching index is obtained by comparing the road tolerance index and the road damage index, and is compared with a preset matching index threshold to determine whether the road damage is normal, specifically:

[0026] Comparing the road tolerance index and the road damage index to obtain a road damage tolerance matching index;

[0027] Comparing the road damage tolerance matching index with a preset matching index threshold to obtain a matching index deviation rate;

[0028] Comparing the matching index deviation rate with a preset matching index deviation rate threshold;

[0029] If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, the road damage normal information is sent;

[0030] If the matching index deviation rate is less than the matching index deviation rate threshold, the road damage abnormality information is sent.

[0031] In a second aspect, the present application provides a road intelligent maintenance system based on equivalent axle times, the system comprising: a memory and a processor, the memory comprising a program of a road intelligent maintenance method based on equivalent axle times, the program of the road intelligent maintenance method based on equivalent axle times implementing the following steps when executed by the processor:

[0032] Obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information based on the road status information;

[0033] According to the road vehicle traffic information, query through a preset equivalent database to obtain equivalent axle data;

[0034] Processing the equivalent axle frequency data through a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data;

[0035] The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model;

[0036] Processing the road damage detection information through a preset road damage degree model to obtain a road damage degree index;

[0037] A comparison process is performed based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index, and the index is compared with a preset matching index threshold to determine whether the road damage is normal.

[0038] Among them, in the road intelligent maintenance system based on equivalent axle times described in the present application, the equivalent axle times data is obtained by querying a preset equivalent database according to the road vehicle traffic information, specifically:

[0039] Extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information;

[0040] The equivalent axle number data is obtained by querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle total load data, axle number coefficient and wheel group coefficient.

[0041] Among them, in the road intelligent maintenance system based on equivalent axle count described in the present application, the equivalent axle count data is processed by a preset axle count statistical model to obtain the accumulated equivalent axle count data, specifically:

[0042] Obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period;

[0043] The equivalent axle-order data is combined with the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

[0044] In the third aspect, the present application also provides a computer-readable storage medium, which includes a road intelligent maintenance method program based on equivalent axle count. When the road intelligent maintenance method program based on equivalent axle count is executed by a processor, the steps of the road intelligent maintenance method based on equivalent axle count as described in any one of the above items are implemented.

[0045] As can be seen from the above, the embodiment of the present application provides a road intelligent maintenance method, system and medium based on equivalent axle times, by obtaining road vehicle traffic information and road status information of a preset area within a preset time period, extracting road design parameter information and road disease detection information according to the road status information, querying through a preset equivalent database according to the road vehicle traffic information, obtaining equivalent axle times data, and then processing through a preset axle times statistical model to obtain cumulative equivalent axle times data, processing through a preset tolerance model according to the road design parameter information combined with the cumulative equivalent axle times data to obtain a road tolerance index, processing through a preset road damage degree model according to the road disease detection information to obtain a road damage degree index, performing comparative processing according to the road tolerance index and the road damage degree index, obtaining a road damage tolerance matching index and comparing it with a preset matching degree index threshold to determine whether the road damage is normal. The present application comprehensively considers the road vehicle traffic information and road status information, realizes the quantitative conversion from raw data to evaluation results, and based on professional knowledge and practical experience, can accurately reflect the actual conditions of the road, help to timely discover potential road problems, and take effective maintenance measures to ensure the comprehensiveness and accuracy of the evaluation. Through automated and intelligent data processing methods, a large amount of road monitoring data can be efficiently processed and evaluation results can be obtained quickly, which helps to improve work efficiency and reduce labor costs.

[0046] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flowchart of a road intelligent maintenance method based on equivalent axle times provided in an embodiment of the present application;

[0049] Figure 2 A flowchart of obtaining equivalent axle-order data in a road intelligent maintenance method based on equivalent axle-order provided in an embodiment of the present application;

[0050] Figure 3 A flowchart of accumulating equivalent axle-time data of a road intelligent maintenance method based on equivalent axle-time provided in an embodiment of the present application;

[0051] Figure 4 A flowchart of obtaining a road tolerance index in a road intelligent maintenance method based on equivalent axle times provided in an embodiment of the present application;

[0052] Figure 5 A flowchart of obtaining a road damage index in a road intelligent maintenance method based on equivalent axle times provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0054] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0055] Please refer to Figure 1 , Figure 1The flowchart of a road intelligent maintenance method based on equivalent axle times in some embodiments of the present application. The road intelligent maintenance method based on equivalent axle times is used in a terminal device, such as a computer, a mobile phone terminal, etc. The road intelligent maintenance method based on equivalent axle times includes the following steps:

[0056] S101, obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information according to the road status information;

[0057] S102, querying a preset equivalent database according to the road vehicle traffic information to obtain equivalent axle data;

[0058] S103, processing the equivalent axle frequency data by using a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data;

[0059] S104, obtaining a road tolerance index by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model;

[0060] S105, processing the road damage detection information through a preset road damage degree model to obtain a road damage degree index;

[0061] S106: Perform a comparison process based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index and compare it with a preset matching index threshold to determine whether the road damage is normal.

[0062] Among them, the present application obtains the road vehicle traffic information and road state information of the preset area within the preset time period, extracts the road design parameter information and road disease detection information according to the road state information, queries the preset equivalent database according to the road vehicle traffic information, obtains the equivalent axle data, and then processes it through the preset axle statistical model to obtain the cumulative equivalent axle data, processes it through the preset tolerance model according to the road design parameter information combined with the cumulative equivalent axle data, obtains the road tolerance index, processes it through the preset road damage model according to the road disease detection information, obtains the road damage index, compares the road tolerance index and the road damage index, obtains the road damage matching index and compares it with the preset matching index threshold to determine whether the road damage is normal. The present application comprehensively considers the road vehicle traffic information and road state information, realizes the quantitative conversion from raw data to evaluation results, and based on professional knowledge and practical experience, can accurately reflect the actual conditions of the road, help to timely discover potential road problems, and take effective maintenance measures to ensure the comprehensiveness and accuracy of the evaluation. Through automated and intelligent data processing methods, a large amount of road monitoring data can be efficiently processed and evaluation results can be quickly obtained. This helps improve work efficiency and reduce labor costs.

[0063] Please refer to Figure 2 , Figure 2 The flowchart of obtaining equivalent axle data in a road intelligent maintenance method based on equivalent axle number in some embodiments of the present application is as follows. According to an embodiment of the present invention, the equivalent axle data is obtained by querying a preset equivalent database based on the road vehicle traffic information, specifically:

[0064] S201, extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information;

[0065] S202, querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient to obtain equivalent axle number data.

[0066] Among them, in order to obtain equivalent axle data, vehicle type data, vehicle size data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient are extracted according to the road vehicle traffic information. Vehicle type data includes cars, trucks, buses, semi-trailers, etc.; vehicle size data includes vehicle length, width, height and other dimensional information; axle load action data refers to the pressure of each axle of the vehicle on the road surface; axle load total load data is the total pressure of all axles of the vehicle on the road surface; axle number coefficient is used to reflect the impact of the number of vehicle axles on road damage; wheel group coefficient takes into account the impact of the arrangement and number of vehicle tires on road damage; and then query through the preset equivalent database to obtain equivalent axle data. The database can search for equivalent axle data that matches the provided vehicle data.

[0067] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the accumulated equivalent axle-trip data in a road intelligent maintenance method based on equivalent axle-trip in some embodiments of the present application is as follows. According to an embodiment of the present invention, the accumulated equivalent axle-trip data is obtained by processing the equivalent axle-trip data through a preset axle-trip statistical model, specifically:

[0068] S301, obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period;

[0069] S302, the equivalent axle-order data is processed by combining the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

[0070] Among them, in order to obtain the cumulative equivalent axle-trip data, it is necessary to consider the changes in road traffic volume and the influence of the design service life and lane coefficient, obtain the road traffic volume growth rate data, road design service life data and road lane coefficient of the preset area within the preset time period, and combine the equivalent axle-trip data with the preset axle-trip statistical model for processing to obtain the cumulative equivalent axle-trip data to reflect the actual use status of the road;

[0071] The calculation formula of the axis statistical model is:

[0072] ,

[0073] in, is the cumulative equivalent axle data, They are the equivalent axle data, road traffic volume growth rate data, road design life data and road lane coefficient. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset equivalent axis database).

[0074] Please refer to Figure 4 , Figure 4 The flowchart of a road tolerance index obtained by a road intelligent maintenance method based on equivalent axle times in some embodiments of the present application. According to an embodiment of the present invention, the road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle times data through a preset tolerance model, specifically:

[0075] S401, extracting design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data, and road bearing capacity data according to the road design parameter information;

[0076] S402, obtaining a road tolerance index by processing the designed speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data, and road bearing capacity data in combination with the cumulative equivalent axle data through a preset tolerance model.

[0077] Among them, in order to obtain the road tolerance index, the design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data and road bearing capacity data are extracted according to the road design parameter information. The design speed data is the speed limit determined during road design based on factors such as road type, traffic flow and safety; the lane size data records the number of lanes, lane width and lane layout of the road; the intersection capacity data includes the intersection type (such as flat intersection, grade-separated intersection), signal control conditions, lane allocation and turning lane settings, etc., and estimates its maximum capacity; the pavement and subgrade material characteristic data records the material of the pavement and subgrade (such as asphalt, concrete, gravel, etc.), structural layer thickness, drainage system and foundation treatment method, etc.; and then combined with the cumulative equivalent axle data, it is processed through a preset tolerance model to obtain the road tolerance index, which is an indicator reflecting the durability and bearing capacity of the road;

[0078] The calculation formula of the tolerance model is:

[0079] ,

[0080] in, is the road tolerance index, is the cumulative equivalent axle data, They are the design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristics data, and road bearing capacity data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0081] Please refer to Figure 5 , Figure 5The flowchart of obtaining the road damage index of a road intelligent maintenance method based on equivalent axle times in some embodiments of the present application is as follows. According to an embodiment of the present invention, the road damage index is obtained by processing the road disease detection information through a preset road damage model, specifically:

[0082] S501, extracting disease type characteristic data, disease degree data, disease total amount data and disease location distribution data according to the road disease detection information;

[0083] S502: Process the damage type characteristic data, the damage degree data, the total damage data and the damage location distribution data through a preset road damage degree model to obtain a road damage degree index.

[0084] Among them, in order to obtain the road damage index, the disease type characteristic data, disease degree data, disease total data and disease location distribution data are extracted according to the road disease detection information. Disease type characteristic data include cracks, potholes, rutting, subsidence, bulges, peeling, etc.; disease degree data include the width, length and depth of cracks, the shape and size of potholes, etc.; total disease data refers to the total number or total area of ​​various diseases in the entire road or a specific section; disease location distribution data records the specific location of the disease on the road, which can be expressed by a coordinate system (such as longitude and latitude) or the distance relative to the starting point of the road, and then processed through a preset road damage model to obtain the road damage index, which is a comprehensive indicator reflecting the overall damage degree and durability of the road;

[0085] The calculation formula of the road damage model is:

[0086] ,

[0087] in, is the road damage index, They are disease type characteristic data, disease severity data, disease total amount data and disease location distribution data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0088] According to an embodiment of the present invention, the road damage tolerance matching index is obtained by comparing the road tolerance index with the road damage index and comparing it with a preset matching index threshold to determine whether the road damage is normal, specifically:

[0089] Comparing the road tolerance index and the road damage index to obtain a road damage tolerance matching index;

[0090] Comparing the road damage tolerance matching index with a preset matching index threshold to obtain a matching index deviation rate;

[0091] Comparing the matching index deviation rate with a preset matching index deviation rate threshold;

[0092] If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, the road damage normal information is sent;

[0093] If the matching index deviation rate is less than the matching index deviation rate threshold, the road damage abnormality information is sent.

[0094] Among them, in order to determine whether there is abnormal damage on the road, a comparison process is performed based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index, which is compared with a preset matching index threshold to obtain a matching index deviation rate, which is then compared with a preset matching index deviation rate threshold. If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, normal road damage information is sent, indicating that the road's damage tolerance performance is within a normal or acceptable range compared with its actual damage degree. If the matching index deviation rate is less than the matching index deviation rate threshold, abnormal road damage information is sent, indicating that the road's damage tolerance performance is poor and does not match its actual damage degree, and there may be potential safety hazards or more frequent maintenance is required.

[0095] The calculation formula of the road damage tolerance matching index is:

[0096] ,

[0097] in, is the road damage tolerance matching index, is the road damage index, is the road tolerance index, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0098] According to an embodiment of the present invention, it also includes:

[0099] Obtain road environment monitoring information within a preset time period;

[0100] Extracting precipitation penetration rate data, temperature sudden change frequency data, sunshine duration data, and geological cracking rate data based on the road environment monitoring information;

[0101] The environmental impact assessment severity index is obtained by processing the precipitation permeability data, temperature sudden change frequency data, sunshine duration data, and geological cracking rate data through an environmental control model;

[0102] The calculation formula of the environmental control model is:

[0103] ,

[0104] in, The environmental impact assessment index is They are respectively the precipitation penetration rate data, the temperature sudden change frequency data, the sunshine duration data, and the geological crack rate data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset environmental control database).

[0105] Among them, in order to evaluate the impact of the environment on road structure and operation, the road environment monitoring information within the preset time period is obtained and the precipitation penetration rate data, temperature sudden change frequency data, light duration data, and geological cracking rate data are extracted. The precipitation penetration rate data reflects the impact of precipitation on road penetration; the temperature sudden change frequency data records the number or amplitude of significant temperature changes in a short period of time, which is used to evaluate the impact of temperature changes on road materials; the light duration data records the sunshine time, especially the duration of strong sunlight, which is used to evaluate the impact of light on the aging of road materials; the geological cracking rate data reflects the crack expansion speed of the geological structure (such as roadbed, slope, etc.), and then processed through the environmental control model to obtain the environmental impact assessment severity index, which is used to reflect the severity of the road environment within the preset time period. The higher the index, the worse the road environment condition, and more stringent maintenance and management measures need to be taken.

[0106] According to an embodiment of the present invention, it also includes:

[0107] Obtain accident frequency data for a preset area;

[0108] comparing the accident frequency data with a preset first accident frequency threshold and a second accident frequency threshold;

[0109] The first accident frequency threshold is less than the second accident frequency threshold;

[0110] If the accident frequency data is less than or equal to the first accident frequency threshold, a normal road operation message is sent;

[0111] If the accident frequency data is greater than the first accident frequency threshold and less than or equal to the second accident frequency threshold, then the road operation abnormality information is sent;

[0112] If the accident frequency data is greater than the second accident frequency threshold, major safety hazard information is sent.

[0113] Among them, in order to improve the service life of the water supply pipeline, the accident frequency data of the preset area is obtained, and compared with the preset first accident frequency threshold and the second accident frequency threshold, respectively, and the first accident frequency threshold is less than the second accident frequency threshold. If the accident frequency data is less than or equal to the first accident frequency threshold, the normal road operation information is sent, indicating that the current road operation is in good condition; if the accident frequency data is greater than the first accident frequency threshold and less than or equal to the second accident frequency threshold, the road operation abnormality information is sent, indicating that the current road operation is abnormal and needs to be paid attention to and certain measures are taken; if the accident frequency data is greater than the second accident frequency threshold, the major safety hazard information is sent, indicating that the current road operation has a major safety hazard and immediate measures need to be taken to intervene to prevent accidents.

[0114] The present invention also discloses a road intelligent maintenance system based on equivalent axle times, comprising a memory and a processor, wherein the memory comprises a road intelligent maintenance method program based on equivalent axle times, and when the road intelligent maintenance method program based on equivalent axle times is executed by the processor, the following steps are implemented:

[0115] Obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information based on the road status information;

[0116] According to the road vehicle traffic information, query through a preset equivalent database to obtain equivalent axle data;

[0117] Processing the equivalent axle frequency data through a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data;

[0118] The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model;

[0119] Processing the road damage detection information through a preset road damage degree model to obtain a road damage degree index;

[0120] A comparison process is performed based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index, and the index is compared with a preset matching index threshold to determine whether the road damage is normal.

[0121] Among them, the present application obtains the road vehicle traffic information and road state information of the preset area within the preset time period, extracts the road design parameter information and road disease detection information according to the road state information, queries the preset equivalent database according to the road vehicle traffic information, obtains the equivalent axle data, and then processes it through the preset axle statistical model to obtain the cumulative equivalent axle data, processes it through the preset tolerance model according to the road design parameter information combined with the cumulative equivalent axle data, obtains the road tolerance index, processes it through the preset road damage model according to the road disease detection information, obtains the road damage index, compares the road tolerance index and the road damage index, obtains the road damage matching index and compares it with the preset matching index threshold to determine whether the road damage is normal. The present application comprehensively considers the road vehicle traffic information and road state information, realizes the quantitative conversion from raw data to evaluation results, and based on professional knowledge and practical experience, can accurately reflect the actual conditions of the road, help to timely discover potential road problems, and take effective maintenance measures to ensure the comprehensiveness and accuracy of the evaluation. Through automated and intelligent data processing methods, a large amount of road monitoring data can be efficiently processed and evaluation results can be quickly obtained. This helps improve work efficiency and reduce labor costs.

[0122] According to an embodiment of the present invention, the equivalent axle data is obtained by querying a preset equivalent database according to the road vehicle traffic information, specifically:

[0123] Extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information;

[0124] The equivalent axle number data is obtained by querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle total load data, axle number coefficient and wheel group coefficient.

[0125] Among them, in order to obtain equivalent axle data, vehicle type data, vehicle size data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient are extracted according to the road vehicle traffic information. Vehicle type data includes cars, trucks, buses, semi-trailers, etc.; vehicle size data includes vehicle length, width, height and other dimensional information; axle load action data refers to the pressure of each axle of the vehicle on the road surface; axle load total load data is the total pressure of all axles of the vehicle on the road surface; axle number coefficient is used to reflect the impact of the number of vehicle axles on road damage; wheel group coefficient takes into account the impact of the arrangement and number of vehicle tires on road damage; and then query through the preset equivalent database to obtain equivalent axle data. The database can search for equivalent axle data that matches the provided vehicle data.

[0126] According to an embodiment of the present invention, the equivalent axle frequency data is processed by a preset axle frequency statistical model to obtain the accumulated equivalent axle frequency data, specifically:

[0127] Obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period;

[0128] The equivalent axle-order data is combined with the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

[0129] Among them, in order to obtain the cumulative equivalent axle-trip data, it is necessary to consider the changes in road traffic volume and the influence of the design service life and lane coefficient, obtain the road traffic volume growth rate data, road design service life data and road lane coefficient of the preset area within the preset time period, and combine the equivalent axle-trip data with the preset axle-trip statistical model for processing to obtain the cumulative equivalent axle-trip data to reflect the actual use status of the road;

[0130] The calculation formula of the axis statistical model is:

[0131] ,

[0132] in, is the cumulative equivalent axle data, They are the equivalent axle data, road traffic volume growth rate data, road design life data and road lane coefficient. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset equivalent axis database).

[0133] According to an embodiment of the present invention, the road design parameter information is combined with the accumulated equivalent axle data and processed by a preset tolerance model to obtain a road tolerance index, which is specifically:

[0134] Extracting design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data, and road bearing capacity data according to the road design parameter information;

[0135] The road tolerance index is obtained by processing the designed speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data and road bearing capacity data in combination with the cumulative equivalent axle data through a preset tolerance model.

[0136] Among them, in order to obtain the road tolerance index, the design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data and road bearing capacity data are extracted according to the road design parameter information. The design speed data is the speed limit determined during road design based on factors such as road type, traffic flow and safety; the lane size data records the number of lanes, lane width and lane layout of the road; the intersection capacity data includes the intersection type (such as flat intersection, grade-separated intersection), signal control conditions, lane allocation and turning lane settings, etc., and estimates its maximum capacity; the pavement and subgrade material characteristic data records the material of the pavement and subgrade (such as asphalt, concrete, gravel, etc.), structural layer thickness, drainage system and foundation treatment method, etc.; and then combined with the cumulative equivalent axle data, it is processed through a preset tolerance model to obtain the road tolerance index, which is an indicator reflecting the durability and bearing capacity of the road;

[0137] The calculation formula of the tolerance model is:

[0138] ,

[0139] in, is the road tolerance index, is the cumulative equivalent axle data, They are the design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristics data, and road bearing capacity data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0140] According to an embodiment of the present invention, the road damage index is obtained by processing the road damage detection information through a preset road damage model, specifically:

[0141] Extracting disease type characteristic data, disease degree data, disease total amount data and disease location distribution data according to the road disease detection information;

[0142] The road damage index is obtained by processing the disease type characteristic data, disease degree data, disease total amount data and disease location distribution data through a preset road damage degree model.

[0143] Among them, in order to obtain the road damage index, the disease type characteristic data, disease degree data, disease total data and disease location distribution data are extracted according to the road disease detection information. Disease type characteristic data include cracks, potholes, rutting, subsidence, bulges, peeling, etc.; disease degree data include the width, length and depth of cracks, the shape and size of potholes, etc.; total disease data refers to the total number or total area of ​​various diseases in the entire road or a specific section; disease location distribution data records the specific location of the disease on the road, which can be expressed by a coordinate system (such as longitude and latitude) or the distance relative to the starting point of the road, and then processed through a preset road damage model to obtain the road damage index, which is a comprehensive indicator reflecting the overall damage degree and durability of the road;

[0144] The calculation formula of the road damage model is:

[0145] ,

[0146] in, is the road damage index, They are disease type characteristic data, disease severity data, disease total amount data and disease location distribution data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0147] According to an embodiment of the present invention, the road damage tolerance matching index is obtained by comparing the road tolerance index with the road damage index and comparing it with a preset matching index threshold to determine whether the road damage is normal, specifically:

[0148] Comparing the road tolerance index and the road damage index to obtain a road damage tolerance matching index;

[0149] Comparing the road damage tolerance matching index with a preset matching index threshold to obtain a matching index deviation rate;

[0150] Comparing the matching index deviation rate with a preset matching index deviation rate threshold;

[0151] If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, the road damage normal information is sent;

[0152] If the matching index deviation rate is less than the matching index deviation rate threshold, the road damage abnormality information is sent.

[0153] Among them, in order to determine whether there is abnormal damage on the road, a comparison process is performed based on the road tolerance index and the road damage index to obtain a road damage tolerance matching index, which is compared with a preset matching index threshold to obtain a matching index deviation rate, which is then compared with a preset matching index deviation rate threshold. If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, normal road damage information is sent, indicating that the road's damage tolerance performance is within a normal or acceptable range compared with its actual damage degree. If the matching index deviation rate is less than the matching index deviation rate threshold, abnormal road damage information is sent, indicating that the road's damage tolerance performance is poor and does not match its actual damage degree, and there may be potential safety hazards or more frequent maintenance is required.

[0154] The calculation formula of the road damage tolerance matching index is:

[0155] ,

[0156] in, is the road damage tolerance matching index, is the road damage index, is the road tolerance index, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset road damage monitoring database).

[0157] According to an embodiment of the present invention, it also includes:

[0158] Obtain road environment monitoring information within a preset time period;

[0159] Extracting precipitation penetration rate data, temperature sudden change frequency data, sunshine duration data, and geological cracking rate data based on the road environment monitoring information;

[0160] The environmental impact assessment severity index is obtained by processing the precipitation permeability data, temperature sudden change frequency data, sunshine duration data, and geological cracking rate data through an environmental control model;

[0161] The calculation formula of the environmental control model is:

[0162] ,

[0163] in, The environmental impact assessment index is They are respectively the precipitation penetration rate data, the temperature sudden change frequency data, the sunshine duration data, and the geological crack rate data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset environmental control database).

[0164] Among them, in order to evaluate the impact of the environment on road structure and operation, the road environment monitoring information within the preset time period is obtained and the precipitation penetration rate data, temperature sudden change frequency data, light duration data, and geological cracking rate data are extracted. The precipitation penetration rate data reflects the impact of precipitation on road penetration; the temperature sudden change frequency data records the number or amplitude of significant temperature changes in a short period of time, which is used to evaluate the impact of temperature changes on road materials; the light duration data records the sunshine time, especially the duration of strong sunlight, which is used to evaluate the impact of light on the aging of road materials; the geological cracking rate data reflects the crack expansion speed of the geological structure (such as roadbed, slope, etc.), and then processed through the environmental control model to obtain the environmental impact assessment severity index, which is used to reflect the severity of the road environment within the preset time period. The higher the index, the worse the road environment condition, and more stringent maintenance and management measures need to be taken.

[0165] According to an embodiment of the present invention, it also includes:

[0166] Obtain accident frequency data for a preset area;

[0167] comparing the accident frequency data with a preset first accident frequency threshold and a second accident frequency threshold;

[0168] The first accident frequency threshold is less than the second accident frequency threshold;

[0169] If the accident frequency data is less than or equal to the first accident frequency threshold, a normal road operation message is sent;

[0170] If the accident frequency data is greater than the first accident frequency threshold and less than or equal to the second accident frequency threshold, then the road operation abnormality information is sent;

[0171] If the accident frequency data is greater than the second accident frequency threshold, major safety hazard information is sent.

[0172] Among them, in order to improve the service life of the water supply pipeline, the accident frequency data of the preset area is obtained, and compared with the preset first accident frequency threshold and the second accident frequency threshold, respectively, and the first accident frequency threshold is less than the second accident frequency threshold. If the accident frequency data is less than or equal to the first accident frequency threshold, the normal road operation information is sent, indicating that the current road operation is in good condition; if the accident frequency data is greater than the first accident frequency threshold and less than or equal to the second accident frequency threshold, the road operation abnormality information is sent, indicating that the current road operation is abnormal and needs to be paid attention to and certain measures are taken; if the accident frequency data is greater than the second accident frequency threshold, the major safety hazard information is sent, indicating that the current road operation has a major safety hazard and immediate measures need to be taken to intervene to prevent accidents.

[0173] The third aspect of the present invention provides a computer-readable storage medium, which includes a road intelligent maintenance method program based on equivalent axle count. When the road intelligent maintenance method program based on equivalent axle count is executed by a processor, the steps of the road intelligent maintenance method based on equivalent axle count as described in any one of the above items are implemented.

[0174] The present invention discloses a road intelligent maintenance method, system and medium based on equivalent axle times, which obtains road vehicle traffic information and road status information of a preset area within a preset time period, extracts road design parameter information and road disease detection information according to the road status information, queries through a preset equivalent database according to the road vehicle traffic information, obtains equivalent axle times data, and then processes through a preset axle times statistical model to obtain cumulative equivalent axle times data, processes through a preset tolerance model according to the road design parameter information combined with the cumulative equivalent axle times data to obtain a road tolerance index, processes through a preset road damage degree model according to the road disease detection information to obtain a road damage degree index, compares and processes the road tolerance index and the road damage degree index, obtains a road damage tolerance matching index and compares it with a preset matching degree index threshold to determine whether the road damage is normal. The present application comprehensively considers road vehicle traffic information and road status information, realizes quantitative conversion from raw data to evaluation results, and can accurately reflect the actual conditions of the road based on professional knowledge and practical experience, helps to timely discover potential road problems, and take effective maintenance measures to ensure the comprehensiveness and accuracy of the evaluation. Through automated and intelligent data processing methods, a large amount of road monitoring data can be efficiently processed and evaluation results can be obtained quickly, which helps to improve work efficiency and reduce labor costs.

[0175] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0176] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0177] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0178] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0179] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A road intelligent maintenance method based on equivalent axle times, characterized in that: The following steps are involved: Obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information based on the road status information; According to the road vehicle traffic information, query through a preset equivalent database to obtain equivalent axle data; Processing the equivalent axle frequency data through a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data; The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model; Extracting disease type characteristic data, disease degree data, disease total amount data and disease location distribution data according to the road disease detection information; The road damage index is obtained by processing the disease type characteristic data, disease degree data, disease total amount data and disease location distribution data through a preset road damage degree model; Comparing the road tolerance index and the road damage index to obtain a road damage tolerance matching index; Comparing the road damage tolerance matching index with a preset matching index threshold to obtain a matching index deviation rate; Comparing the matching index deviation rate with a preset matching index deviation rate threshold; If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, the road damage normal information is sent; If the matching index deviation rate is less than the matching index deviation rate threshold, the road damage abnormality information is sent; The calculation formula of the road damage tolerance matching index is: ； in, is the road damage tolerance matching index, is the road damage index, is the road tolerance index, is the preset characteristic coefficient.

2. The road intelligent maintenance method based on equivalent axle times according to claim 1 is characterized in that: The equivalent axle data is obtained by querying a preset equivalent database according to the road vehicle traffic information, specifically: Extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information; The equivalent axle number data is obtained by querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle total load data, axle number coefficient and wheel group coefficient.

3. The road intelligent maintenance method based on equivalent axle times according to claim 2 is characterized in that: The equivalent axle frequency data is processed by a preset axle frequency statistical model to obtain the accumulated equivalent axle frequency data, specifically: Obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period; The equivalent axle-order data is combined with the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

4. The road intelligent maintenance method based on equivalent axle times according to claim 3 is characterized in that: The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle data through a preset tolerance model, specifically: Extracting design speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data, and road bearing capacity data according to the road design parameter information; The road tolerance index is obtained by processing the designed speed data, lane size data, intersection capacity data, pavement and subgrade material characteristic data and road bearing capacity data in combination with the cumulative equivalent axle data through a preset tolerance model.

5. A road intelligent maintenance system based on equivalent axle times, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a road intelligent maintenance method program based on equivalent axle times, and the road intelligent maintenance method program based on equivalent axle times is executed by the processor to implement the following steps: Obtaining road vehicle traffic information and road status information in a preset area within a preset time period, and extracting road design parameter information and road disease detection information based on the road status information; According to the road vehicle traffic information, query through a preset equivalent database to obtain equivalent axle data; Processing the equivalent axle frequency data through a preset axle frequency statistical model to obtain cumulative equivalent axle frequency data; The road tolerance index is obtained by processing the road design parameter information in combination with the accumulated equivalent axle-load data through a preset tolerance model; Extracting disease type characteristic data, disease degree data, disease total amount data and disease location distribution data according to the road disease detection information; The road damage index is obtained by processing the disease type characteristic data, disease degree data, disease total amount data and disease location distribution data through a preset road damage degree model; Comparing the road tolerance index and the road damage index to obtain a road damage tolerance matching index; Comparing the road damage tolerance matching index with a preset matching index threshold to obtain a matching index deviation rate; Comparing the matching index deviation rate with a preset matching index deviation rate threshold; If the matching index deviation rate is greater than or equal to the matching index deviation rate threshold, the road damage normal information is sent; If the matching index deviation rate is less than the matching index deviation rate threshold, the road damage abnormality information is sent; The calculation formula of the road damage tolerance matching index is: ； in, is the road damage tolerance matching index, is the road damage index, is the road tolerance index, is the preset characteristic coefficient.

6. The road intelligent maintenance system based on equivalent axle times according to claim 5 is characterized in that: The equivalent axle data is obtained by querying a preset equivalent database according to the road vehicle traffic information, specifically: Extracting vehicle type data, vehicle size specification data, axle load action data, axle load total load data, axle number coefficient and wheel group coefficient according to the road vehicle traffic information; The equivalent axle number data is obtained by querying a preset equivalent database according to the vehicle type data, vehicle size specification data, axle load action data, axle total load data, axle number coefficient and wheel group coefficient.

7. The road intelligent maintenance system based on equivalent axle times according to claim 6 is characterized in that: The equivalent axle frequency data is processed by a preset axle frequency statistical model to obtain the accumulated equivalent axle frequency data, specifically: Obtaining road traffic volume growth rate data, road design life data and road lane coefficient in a preset area within the preset time period; The equivalent axle-order data is combined with the road traffic volume growth rate data, the road design life data and the road lane coefficient through a preset axle-order statistical model to obtain the cumulative equivalent axle-order data.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a road intelligent maintenance method program based on equivalent axle count. When the road intelligent maintenance method program based on equivalent axle count is executed by a processor, the steps of the road intelligent maintenance method based on equivalent axle count as described in any one of claims 1 to 4 are implemented.

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