CN117078234B - Optimized road network maintenance method, electronic equipment and storage medium - Google Patents

Optimized road network maintenance method, electronic equipment and storage medium Download PDF

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CN117078234B
CN117078234B CN202311336731.9A CN202311336731A CN117078234B CN 117078234 B CN117078234 B CN 117078234B CN 202311336731 A CN202311336731 A CN 202311336731A CN 117078234 B CN117078234 B CN 117078234B
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road
void
area
average
network maintenance
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CN117078234A (en
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杨宇星
陈振武
贾磊
安茹
孟安鑫
阚倩
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Shenzhen Urban Transport Planning Center Co Ltd
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Abstract

An optimized road network maintenance method, electronic equipment and storage medium belong to the field of road maintenance method calculation. To make a reasonable road maintenance priority scheme facing the road network. The invention constructs a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, adopts the ground penetrating radar to collect road internal images, carries out disease recognition on the collected road internal images by using the road internal void disease recognition convolutional neural network model, and calculates road internal void region parameters; constructing an evaluation index of the service life in the road, carrying out normalization treatment, and constructing a comprehensive evaluation index of the service life in the road; performing preliminary road internal service life assessment; constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function and optimizing the life distribution function model; constructing a road network maintenance scoring matrix, and judging the priority level of road network maintenance. The invention improves the road maintenance efficiency and reasonably optimizes the distribution of maintenance resources.

Description

Optimized road network maintenance method, electronic equipment and storage medium
Technical Field
The invention belongs to the field of road maintenance and repair method calculation, and particularly relates to an optimized road network maintenance and repair method, electronic equipment and a storage medium.
Background
The road is the basis for safe driving of the vehicle. The good road technical condition has important significance for guaranteeing the safe running of the vehicle. In the technical condition assessment process of the road in the industry, technical condition grade assessment is carried out on the basis of road inspection on the road surface diseases including road surface diseases such as cracks, pits, ruts, subsidence, waves, hugs and the like so as to guide the road maintenance decision. The road mileage in China is long, the road maintenance department has the problems of large work load of maintenance personnel and lack of maintenance funds, and in the road network maintenance process, road maintenance and maintenance sequencing is usually carried out according to maintenance cost, so that the work with low maintenance cost is arranged at a front position; or based on subjective experience of the decision maker, by prioritizing a certain index of interest to the decision maker. The method has strong subjectivity and is difficult to ensure the optimization of decision effect.
The invention relates to an Internet of things system for monitoring and early warning of urban road void and subsidence, which has the application number of 202210685300.2 and is characterized in that a plurality of sensing nodes are embedded in soil body in a distributed manner and used for monitoring the state of the soil body in real time, analyzing the migration state of the soil body and judging and identifying whether a cavity subsidence and subsidence area is formed.
The invention patent with the application number of 202110686172.9 and the invention name of a method for evaluating and pre-controlling the safety margin of a road in the condition of urban underground construction adopts a simulation mode to simulate the development process of a cavity under underground construction vibration, determines the least favorable load working condition of a driving based on the simulated cavity expansion form, establishes a road damage criterion, determines the actual bearing capacity of the road in the condition of disaster, calculates the safety margin of the road in the condition of the disaster, and further performs the combined pre-control on the ground and underground according to the result.
The invention patent with the application number of 202111013320.7 and the invention name of a highway network maintenance planning method based on maintenance priority ordering is used for determining the maintenance property of each road section by collecting the basic information of each road section in a highway network, providing a maintenance priority ordering method, and determining the maintenance planning of the next few years by combining maintenance funds and the road surface technical condition prediction information of each road section.
The method is mainly oriented to collapse and forecast and early warning of the road cavity, and is only suitable for being used when the interior is hollow or the cavity is developed to a serious degree, and at the moment, the potential safety hazard of the vehicle running on the road has a great threat. Meanwhile, in the road network maintenance decision process, a scientific and reasonable maintenance sequence determining method is lacked, particularly, the difference between expected service lives of roads is not considered, the road network maintenance decision scheme is high in subjectivity, so that optimal configuration of economy and benefit is difficult to realize for maintenance funds, and finally, the phenomena of unreasonable maintenance, waste of maintenance funds and the like are caused.
Disclosure of Invention
The invention aims to solve the problem of formulating a reasonable road maintenance priority scheme oriented to a road network, and provides an optimized road network maintenance method, electronic equipment and a storage medium.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
an optimized road network maintenance method comprises the following steps:
s1, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
s2, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S1;
s3, carrying out normalization processing on the road internal service life assessment index obtained in the step S2, and constructing a road internal service life comprehensive assessment index;
s4, calculating comprehensive evaluation indexes of the service life of the interior of the road according to the method of the step S3, and performing preliminary evaluation of the service life of the interior of the road;
s5, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function;
S6, optimizing the life distribution function model of the road section obtained in the step S5 to obtain an optimized life distribution function model of the road section, and calculating the average damage occurrence time of the road section;
s7, setting the average damage occurrence time, the road technical condition grade, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume and the road age of the road section obtained in the step S6 as road network maintenance factors, constructing a road network maintenance scoring matrix, and judging the road network maintenance priority;
the specific implementation method of the step S7 comprises the following steps:
s7.1, setting the average damage occurrence time, road technical condition grade, road width, road length, road material, road grade, maintenance cost, traffic volume and road age of the road section obtained in the step S6 as road network maintenance factors, wherein the number of the road network maintenance factors is 9;
s7.2, setting the number of roads in the road network as n, scoring road network maintenance factors of each road by adopting a manual assessment mode, constructing a road network maintenance scoring matrix PF, and calculating the expression as follows:
wherein pf is ij Scoring the ith road and the jth road network maintenance factors in the road network maintenance scoring matrix;
s7.3, carrying out standardized processing on indexes in all road network maintenance scoring matrixes obtained in the step S7.2, wherein the calculation expression is as follows:
wherein,a standardized score for the ith road and the jth road network maintenance factors;
on the basis, a road network maintenance standardized scoring matrix PF is obtained b The computational expression is:
s7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
wherein PFS j Road network maintenance scoring entropy for the jth road network maintenance factors;
s7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
wherein Fw j Road network maintenance influence factors for the jth road network maintenance factors;
s7.6, calculating a final score PFF corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5, wherein the calculation expression is as follows:
PFF＝PF×Fw T
wherein Fw T And as the transposed matrix of Fw, arranging the final scores corresponding to the roads in the order from small to large, wherein the smaller the score is, the higher the maintenance priority is.
Further, the specific implementation method of the step S1 includes the following steps:
S1.1, constructing a road internal void disease recognition convolutional neural network model;
s1.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
s1.1.2 the road internal disease data set obtained in the step S1.1.1 is randomly divided into a training set, a verification set and a test set according to the proportion of 6:2:2;
s1.1.3, inputting the training set, the verification set and the test set obtained in the step S1.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of a convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain a road internal void disease identification convolutional neural network model;
s1.2, acquiring an image of the interior of a road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a road interior void area;
S1.2.1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing disease identification on the acquired internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road obtained in the step S1.1 to obtain an internal image of the damaged road;
s1.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill step S1.2.1, and obtaining a road void area;
s1.2.3 the endoscope is put into the road void region, the top plate position and the bottom plate position of the road void region are determined by the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the height H of the road void region ha ；
S1.2.4 then filling water into the road void region obtained in step S1.2.2 until the water is filled, and recording the volume of the water filling as the volume V of the road void region a ；
S1.2.5 calculating the area S of the road void a The computational expression is:
further, the specific implementation method of the step S2 includes the following steps:
s2.1, setting the total number of the road void areas as N T ；
S2.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
S2.2.1 average height of road void areaThe calculated expression of (2) is:
wherein the height of the kth void area of the road is
S2.2.2 average volume of road void areaThe calculated expression of (2) is:
wherein the volume of the kth void area of the road is
S2.2.3 average area of road void areaThe calculated expression of (2) is:
wherein the area of the kth void area of the road is
S2.2.4 gradient of altitude change in road voidThe calculated expression of (2) is:
wherein t is l 、t l+1 The first data acquisition time and the first (1) data acquisition time,for the average height of the road void area at the first data acquisition time, +.>The average height of the road void area at the 1+1st data acquisition time;
s2.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
wherein,for the average volume of the road void area at the first data acquisition time, +.>The average volume of the road void area for the 1+1st data acquisition time;
s2.2.6 number change gradient dN of road void areas T The calculated expression of (2) is:
wherein,the number of road void areas for the first data acquisition time, < >>The number of road void areas for the 1+1st data acquisition time;
S2.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
wherein,for the average area of the road void area at the first data acquisition time, +.>The average area of the road void area for the 1+1st data acquisition time.
Further, the specific implementation method of the step S3 includes the following steps:
s3.1, dividing the road into NP sections by taking 50m as the road dividing length, wherein NP is the total number of road sections;
s3.2, taking a time point at which road construction starts to operate as a time zero point, respectively detecting a road void area of each section of road after the running time t of each section of road, and calculating a road internal service life evaluation index of each section of road void area;
s3.3, traversing the road interior of each road void area calculated in the step S3.2 to enableObtaining the maximum value of the service life evaluation indexes in each road, including the maximum value of the total number of the road void areasAverage height maximum value of road void area +.>Average volume maximum>Average area maximum->Maximum value of the gradient of the height change->Maximum value of volume change gradient>Maximum value of the number change gradient>Maximum value of area change gradient- >
S3.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S3.2 by using the maximum value of the road internal service life evaluation index of each road void area obtained in the step S3.3 as a reference, wherein the calculation formula is as follows:
wherein,the average height of the road void area is normalized;
wherein,the total number of road void areas processed for normalization;
wherein,an average volume of the road void area for normalization processing;
wherein,an average area of the road void area which is normalized;
wherein,a height change gradient of the road void area which is normalized;
wherein,a number change gradient of the road void area which is normalized;
wherein,a volume change gradient of a road void area which is normalized;
wherein,an area change gradient of a road void area which is normalized;
then, establishing a comprehensive evaluation index TK of the service life in the road, wherein the calculation expression is as follows:
wherein zk1 is a weight coefficient of an average height of the normalized road void areas, zk2 is a weight coefficient of a total number of the normalized road void areas, zk3 is a weight coefficient of an average volume of the normalized road void areas, zk4 is a weight coefficient of an average area of the normalized road void areas, zk5 is a weight coefficient of a height change gradient of the normalized road void areas, zk6 is a weight coefficient of a number change gradient of the normalized road void areas, zk7 is a weight coefficient of a volume change gradient of the normalized road void areas, zk8 is a weight coefficient of an area change gradient of the normalized road void areas;
S3.5 according to step S3.4 calculating comprehensive evaluation index of service life of the f road section in the road at time ti by using method
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, counting road detection data of MK roads after 15 years of running, calculating comprehensive evaluation indexes of the service life inside each road according to the method of the step S3,for the comprehensive evaluation index of the service life of the interior of the road after 15 years of operation of the jth road, then calculating the average value of the comprehensive evaluation index of the service life of the interior of the road of the MK road ≡>Standard deviation of comprehensive evaluation index of service life in road>
S4.2, constructing a threshold TK of a comprehensive evaluation index of the service life in the road AT The computational expression is:
when (when)If yes, judging that the interior of the road is intact, if yes>Judging that the interior of the road is damaged;
s4.3, collecting the time when the 1 st damage occurs to the ith road section of the road section judged to be damaged in the step S4.2Then, a time matrix FT of the 1 st occurrence of damage of the road section is established,>
s4.4, counting the road operation t i Number of road segments NDt with time corresponding to 1 st occurrence of damaged road segments i Then calculate the road operation t i The number of damaged road sections in the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections, and the calculation expression is as follows:
Wherein BP is ti For road operation t i The number of damaged road segments 1 st time of the road segments corresponding in time is a percentage of the total number of road segments.
Further, the specific implementation method of the step S5 includes the following steps:
s5.1, constructing a life distribution function model of a road section by adopting a three-parameter Weibull function, wherein the calculation expression is as follows:
wherein t is the service time of the road section, a s Position parameter of Weibull distribution, b s Is the scale parameter of Weibull distribution, c s Is the shape parameter of Weibull distribution, B S (t) the probability of accumulated damage of the road section in the service time of the road section;
s5.2, constructing a probability density function model of life distribution of the road section based on the life distribution function model of the road section in the step S5.1, wherein the calculation expression is as follows:
wherein C is S (t) the probability of damage to the road section corresponding to the moment t in the service time of the road section;
constructing a reliability function model of a road section, wherein the calculation expression is as follows:
wherein D is S (t) is the probability that the road segment is in good condition within the service time of the road segment;
constructing a failure probability function model of a road section, wherein the calculation expression is as follows:
wherein E is S (t) is the probability that the road section is not damaged before the service time t of the road section and the damage occurs in the unit time of the road section after the service time t of the road section;
Constructing a time function model of average damage of a road section, wherein the calculation expression is as follows:
wherein H is S And (t) the average damage occurrence time of the road section in the service time of the road section.
Further, the specific implementation method of the step S6 includes the following steps:
s6.1, establishing parameter a s 、b s 、c s And the relation with the service life of the road, and the calculation expression is as follows:
wherein t is min Is a roadMinimum road life of segment, d 1s 、e 1s 、f 1s A is respectively a s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
b s ＝d 2s (Δt) 2 +e 2s (Δt)+f 2s
wherein deltat is the difference between the maximum value and the minimum value of the service life of the road in the road section; d, d 2s 、e 2s 、f 2s B is respectively s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
Δt＝t max -t min
wherein t is av Is the average value of the service life of the road in the road section, d 3s 、e 3s 、f 3s C respectively s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
s6.2, parameter a constructed based on step S6.1 s 、b s 、c s Optimizing the service life distribution function model of the road section obtained in the step S5 according to the relation between the service life of the road and the service life of the road;
the calculation expression of the life distribution function model of the optimized road section is as follows:
the calculation expression of the probability density function model of the life distribution of the optimized road section is as follows:
The calculation expression of the reliability function model of the optimized road section is as follows:
the calculation expression of the failure probability function model of the optimized road section is as follows:
the calculation expression of the time function model of the average damage occurrence of the optimized road section is as follows:
s6.3 BP obtained in step S4 ti And ti, carrying out a life distribution function model of the road section constructed in the step S5.1, adopting a least square method to fit and solve, and calculating to obtain a s 、b s 、c s ；
A based on step S6.1 s Relationship with road life, calculated a s And statistically derivedAdopting least square fitting solution, and calculating to obtain d 1s 、e 1s 、f 1s ；
B based on step S6.1 s Relationship with road service life, calculated b s And the obtained delta t is subjected to fitting solution by a least square method, and d is calculated and obtained 2s 、e 2s 、f 2s ；
C based on step S6.1 s Relationship with road life, calculated c s And t is obtained av Solving by least square fitting, and calculating to obtain d 3s 、e 3s 、f 3s 。
The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the optimized road network maintenance method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the described optimized road network maintenance method.
The invention has the beneficial effects that:
the optimized road network maintenance method provided by the invention ensures that an optimal maintenance decision scheme can be made under the condition of limited maintenance funds, thereby effectively ensuring the safe operation of roads. In the invention, a road internal state evaluation index system is established on the basis of identifying and index calculating the void areas in the road, and the index system comprises the number, height, volume and area of the void areas and the change gradient of the corresponding indexes. Meanwhile, considering the difference of influence factors among different road segments, providing a road surface damage evaluation method and constructing a damage time matrix; the calculation methods such as a life distribution function, a probability density function, a reliability function, a failure probability function, an average time of damage occurrence and the like suitable for road life analysis are provided by means of the statistical analysis of the duty ratio of the damaged road section to the total road section and the combination of the Weibull distribution function, so that the calculation of the life of each road section is realized, and accurate and scientific decision support can be provided for maintenance and repair of road surfaces. Finally, a maintenance and repair sequence determining method oriented to the road network is constructed based on the prediction of the average time of the occurrence of the damage of different road segments, and the effectiveness of maintenance and repair decisions is improved.
The optimized road network maintenance method is based on the service life of the interior of the road, the road network maintenance method is provided, the road maintenance efficiency can be improved, the distribution and the use of maintenance resources are more reasonably optimized, the maintenance time and the cost are reduced, and the maintenance quality is improved by reasonably planning the maintenance scheme. Meanwhile, the deterioration and diffusion of diseases in the road are avoided, traffic accidents and road congestion are avoided, the driving safety of the road is improved, and the service life of the road is prolonged. The road network maintenance system not only can meet the maintenance targets of safety, comfort and durability of the road, orderly improve the overall technical level of the road network, realize smooth, coordinated and sustainable development of the road network, but also can improve the use efficiency and scientific decision level of maintenance funds, ensure the maximization of the investment benefit of the maintenance funds, and realize the transition from passive maintenance to active maintenance and scientific maintenance.
Drawings
Fig. 1 is a flowchart of an optimized road network maintenance method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and detailed description. It should be understood that the embodiments described herein are for purposes of illustration only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations, and the present invention can have other embodiments as well.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
For further understanding of the invention, the following detailed description is presented in conjunction with the accompanying drawings 1 to provide a further understanding of the invention in its aspects, features and efficacy:
the first embodiment is as follows:
an optimized road network maintenance method comprises the following steps:
s1, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
further, the specific implementation method of the step S1 includes the following steps:
s1.1, constructing a road internal void disease recognition convolutional neural network model;
S1.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
s1.1.2 the road internal disease data set obtained in the step S1.1.1 is randomly divided into a training set, a verification set and a test set according to the proportion of 6:2:2;
s1.1.3, inputting the training set, the verification set and the test set obtained in the step S1.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of a convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain a road internal void disease identification convolutional neural network model;
s1.2, acquiring an image of the interior of a road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a road interior void area;
s1.2.1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing disease identification on the acquired internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road obtained in the step S1.1 to obtain an internal image of the damaged road;
S1.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill step S1.2.1, and obtaining a road void area;
s1.2.3 the endoscope is put into the road void region, the top plate position and the bottom plate position of the road void region are determined by the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the height H of the road void region ha ；
S1.2.4 then filling water into the road void region obtained in step S1.2.2 until the water is filled, and recording the volume of the water filling as the volume V of the road void region a ；
S1.2.5 calculating the area S of the road void a The computational expression is:
/>
s2, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S1;
further, the specific implementation method of the step S2 includes the following steps:
s2.1, setting the total number of the road void areas as N T ；
S2.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
s2.2.1 average height of road void area The calculated expression of (2) is:
wherein the height of the kth void area of the road is
S2.2.2 average volume of road void areaThe calculated expression of (2) is:
wherein the volume of the kth void area of the road is
S2.2.3 average area of road void areaThe calculated expression of (2) is:
wherein the area of the kth void area of the road is
S2.2.4 gradient of altitude change in road voidThe calculated expression of (2) is:
wherein t is l 、t l+1 The first data acquisition time and the first (1) data acquisition time,for the average height of the road void area at the first data acquisition time, +.>The average height of the road void area at the 1+1st data acquisition time;
s2.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
wherein,for the average volume of the road void area at the first data acquisition time, +.>The average volume of the road void area for the 1+1st data acquisition time;
s2.2.6 number change gradient dN of road void areas T The calculated expression of (2) is:
/>
wherein,track for the first data acquisition timeNumber of road void areas, +.>The number of road void areas for the 1+1st data acquisition time;
S2.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
wherein,for the average area of the road void area at the first data acquisition time, +.>The average area of the road void area at the 1+1st data acquisition time;
s3, carrying out normalization processing on the road internal service life assessment index obtained in the step S2, and constructing a road internal service life comprehensive assessment index;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, dividing the road into NP sections by taking 50m as the road dividing length, wherein NP is the total number of road sections;
s3.2, taking a time point at which road construction starts to operate as a time zero point, respectively detecting a road void area of each section of road after the running time t of each section of road, and calculating a road internal service life evaluation index of each section of road void area;
s3.3, traversing the road internal service life evaluation indexes of each road void area calculated in the step S3.2 to obtain the maximum value of the road internal service life evaluation indexes, including the total number maximum value of the road void areasAverage height maximum value of road void area +.>Average volume maximum >Average area maximum->Maximum value of the gradient of the height change->Maximum value of volume change gradient>Maximum value of the number change gradient>Maximum value of area change gradient->
S3.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S3.2 by using the maximum value of the road internal service life evaluation index of each road void area obtained in the step S3.3 as a reference, wherein the calculation formula is as follows:
wherein,the average height of the road void area is normalized;
wherein,the total number of road void areas processed for normalization;
wherein,an average volume of the road void area for normalization processing;
wherein,an average area of the road void area which is normalized; />
Wherein,a height change gradient of the road void area which is normalized;
wherein,number of road void areas for normalizationA varying gradient;
wherein,a volume change gradient of a road void area which is normalized;
wherein,an area change gradient of a road void area which is normalized;
then, establishing a comprehensive evaluation index TK of the service life in the road, wherein the calculation expression is as follows:
Wherein zk1 is a weight coefficient of an average height of the normalized road void areas, zk2 is a weight coefficient of a total number of the normalized road void areas, zk3 is a weight coefficient of an average volume of the normalized road void areas, zk4 is a weight coefficient of an average area of the normalized road void areas, zk5 is a weight coefficient of a height change gradient of the normalized road void areas, zk6 is a weight coefficient of a number change gradient of the normalized road void areas, zk7 is a weight coefficient of a volume change gradient of the normalized road void areas, zk8 is a weight coefficient of an area change gradient of the normalized road void areas;
s3.5, calculating comprehensive evaluation indexes of service lives of the f-th road section in the road at the time ti according to the method of the step S3.4
S4, calculating comprehensive evaluation indexes of the service life of the interior of the road according to the method of the step S3, and performing preliminary evaluation of the service life of the interior of the road;
further, the specific implementation method of the step S4 includes the following steps:
s4.1, counting road detection data of MK roads after 15 years of running, calculating comprehensive evaluation indexes of the service life inside each road according to the method of the step S3, For the comprehensive evaluation index of the service life of the interior of the road after 15 years of operation of the jth road, then calculating the average value of the comprehensive evaluation index of the service life of the interior of the road of the MK road ≡>Standard deviation of comprehensive evaluation index of service life in road>
S4.2, constructing a threshold TK of a comprehensive evaluation index of the service life in the road AT The computational expression is:
when (when)If yes, judging that the interior of the road is intact, if yes>Judging that the interior of the road is damaged;
s4.3, collecting the time when the 1 st damage occurs to the ith road section of the road section judged to be damaged in the step S4.2Then, a time matrix FT of the 1 st occurrence of damage of the road section is established,>
s4.4, counting the road operation t i Number of road segments NDt with time corresponding to 1 st occurrence of damaged road segments i Then calculate the road operation t i The number of damaged road sections in the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections, and the calculation expression is as follows:
wherein BP is ti For road operation t i The number of damaged road sections of the 1 st time of the road sections corresponding to time accounts for the percentage of the total number of the road sections;
s5, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function;
Further, the specific implementation method of the step S5 includes the following steps:
s5.1, constructing a life distribution function model of a road section by adopting a three-parameter Weibull function, wherein the calculation expression is as follows:
wherein t is the service time of the road section, a s Position parameter of Weibull distribution, b s Is the scale parameter of Weibull distribution, c s Is the shape parameter of Weibull distribution, B S (t) the probability of accumulated damage of the road section in the service time of the road section;
s5.2, constructing a probability density function model of life distribution of the road section based on the life distribution function model of the road section in the step S5.1, wherein the calculation expression is as follows:
wherein C is S (t) the probability of damage to the road section corresponding to the moment t in the service time of the road section;
constructing a reliability function model of a road section, wherein the calculation expression is as follows:
wherein D is S (t) is the probability that the road segment is in good condition within the service time of the road segment;
constructing a failure probability function model of a road section, wherein the calculation expression is as follows:
wherein E is S (t) is the probability that the road section is not damaged before the service time t of the road section and the damage occurs in the unit time of the road section after the service time t of the road section;
Constructing a time function model of average damage of a road section, wherein the calculation expression is as follows:
wherein H is S (t) is the average damage occurrence time of the road section in the service time of the road section;
s6, optimizing the life distribution function model of the road section obtained in the step S5 to obtain an optimized life distribution function model of the road section, and calculating the average damage occurrence time of the road section;
further, the method comprises the steps of,
the specific implementation method of the step S6 comprises the following steps:
s6.1, establishing parameter a s 、b s 、c s And the relation with the service life of the road, and the calculation expression is as follows:
wherein t is min Is the minimum value of the service life of the road section, d 1s 、e 1s 、f 1s A is respectively a s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
b s ＝d 2s (Δt) 2 +e 2s (Δt)+f 2s
wherein deltat is the difference between the maximum value and the minimum value of the service life of the road in the road section; d, d 2s 、e 2s 、f 2s B is respectively s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
Δt＝t max -t min
wherein t is av Is the average value of the service life of the road in the road section, d 3s 、e 3s 、f 3s C respectively s Secondary term regression parameters, primary term regression parameters, constant term regression parameters;
s6.2, parameter a constructed based on step S6.1 s 、b s 、c s Optimizing the service life distribution function model of the road section obtained in the step S5 according to the relation between the service life of the road and the service life of the road;
The calculation expression of the life distribution function model of the optimized road section is as follows:
the calculation expression of the probability density function model of the life distribution of the optimized road section is as follows:
the calculation expression of the reliability function model of the optimized road section is as follows:
/>
the calculation expression of the failure probability function model of the optimized road section is as follows:
the calculation expression of the time function model of the average damage occurrence of the optimized road section is as follows:
s6.3 BP obtained in step S4 ti And ti, carrying out a life distribution function model of the road section constructed in the step S5.1, adopting a least square method to fit and solve, and calculating to obtain a s 、b s 、c s ；
Based onA obtained in step S6.1 s Relationship with road life, calculated a s And statistically derivedAdopting least square fitting solution, and calculating to obtain d 1s 、e 1s 、f 1s ；
B based on step S6.1 s Relationship with road service life, calculated b s And the obtained delta t is subjected to fitting solution by a least square method, and d is calculated and obtained 2s 、e 2s 、f 2s ；
C based on step S6.1 s Relationship with road life, calculated c s And t is obtained av Solving by least square fitting, and calculating to obtain d 3s 、e 3s 、f 3s ；
S7, setting the average damage occurrence time, the road technical condition grade, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume and the road age of the road section obtained in the step S6 as road network maintenance factors, constructing a road network maintenance scoring matrix, and judging the road network maintenance priority;
Further, the specific implementation method of the step S7 includes the following steps:
s7.1, setting the average damage occurrence time, road technical condition grade, road width, road length, road material, road grade, maintenance cost, traffic volume and road age of the road section obtained in the step S6 as road network maintenance factors, wherein the number of the road network maintenance factors is 9;
s7.2, setting the number of roads in the road network as n, scoring road network maintenance factors of each road by adopting a manual assessment mode, constructing a road network maintenance scoring matrix PF, and calculating the expression as follows:
wherein pf is ij Scoring the ith road and the jth road network maintenance factors in the road network maintenance scoring matrix;
s7.3, carrying out standardized processing on indexes in all road network maintenance scoring matrixes obtained in the step S7.2, wherein the calculation expression is as follows:
wherein,a standardized score for the ith road and the jth road network maintenance factors;
on the basis, a road network maintenance standardized scoring matrix PF is obtained b The computational expression is:
s7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
wherein PFS j Road network maintenance scoring entropy for the jth road network maintenance factors;
S7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
wherein Fw j Road network maintenance influence factors for the jth road network maintenance factors;
s7.6, calculating a final score PFF corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5, wherein the calculation expression is as follows:
PFF＝PF×Fw T
wherein Fw T And as the transposed matrix of Fw, arranging the final scores corresponding to the roads in the order from small to large, wherein the smaller the score is, the higher the maintenance priority is.
According to the optimized road network maintenance method, under the condition that maintenance funds are limited, an optimal maintenance decision scheme can be made, and road safety operation is further effectively guaranteed. In the invention, a road internal state evaluation index system is established on the basis of identifying and index calculating the void areas in the road, and the index system comprises the number, height, volume and area of the void areas and the change gradient of the corresponding indexes. Meanwhile, considering the difference of influence factors among different road segments, providing a road surface damage evaluation method and constructing a damage time matrix; the calculation methods such as a life distribution function, a probability density function, a reliability function, a failure probability function, an average time of damage occurrence and the like suitable for road life analysis are provided by means of the statistical analysis of the duty ratio of the damaged road section to the total road section and the combination of the Weibull distribution function, so that the calculation of the life of each road section is realized, and accurate and scientific decision support can be provided for maintenance and repair of road surfaces. Finally, a maintenance and repair sequence determining method oriented to the road network is constructed based on the prediction of the average time of the occurrence of the damage of different road segments, and the effectiveness of maintenance and repair decisions is improved.
The second embodiment is as follows:
the electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the optimized road network maintenance method when executing the computer program.
The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the optimized road network maintenance method when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
And a third specific embodiment:
a computer readable storage medium having stored thereon a computer program which when executed by a processor implements an optimized road network maintenance repair method according to any one of the claims.
The computer readable storage medium of the present invention may be any form of storage medium readable by a processor of a computer device, including but not limited to, nonvolatile memory, volatile memory, ferroelectric memory, etc., having a computer program stored thereon, which when read and executed by the processor of the computer device, implements the steps of an optimized road network maintenance method as described above.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the present application has been described hereinabove with reference to specific embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the embodiments disclosed in this application may be combined with each other in any way as long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification merely for the sake of omitting the sake of brevity and saving resources. Therefore, it is intended that the present application not be limited to the particular embodiments disclosed, but that the present application include all embodiments falling within the scope of the appended claims.

Claims (4)

1. An optimized road network maintenance method is characterized in that: the method comprises the following steps:
s1, constructing a road internal void disease recognition convolutional neural network model based on a ground penetrating radar void disease image, then acquiring a road internal image by adopting the ground penetrating radar, performing disease recognition on the acquired road internal image by utilizing the acquired road internal void disease recognition convolutional neural network model, and calculating road internal void region parameters;
the specific implementation method of the step S1 comprises the following steps:
s1.1, constructing a road internal void disease recognition convolutional neural network model;
s1.1.1, establishing a road internal disease data set based on a ground penetrating radar void disease image: marking diseases in the ground penetrating radar void disease images and marking disease categories by using LabelImg software, and storing the names of marking files consistent with the names of the ground penetrating radar void disease images to obtain a road internal disease data set;
s1.1.2 the road internal disease data set obtained in the step S1.1.1 is randomly divided into a training set, a verification set and a test set according to the proportion of 6:2:2;
s1.1.3, inputting the training set, the verification set and the test set obtained in the step S1.1.2 into a convolutional neural network for training, verifying and testing, and outputting model parameters of a convolutional neural network model, including the number of network layers, the number of neuron nodes of each layer, the learning rate, the weight, the bias, the activation function, the loss function and the convolution kernel, so as to obtain a road internal void disease identification convolutional neural network model;
S1.2, acquiring an image of the interior of a road, identifying the disease by using the obtained model of the road interior void disease identification convolutional neural network, and calculating parameters of a road interior void area;
s1.2.1, acquiring an internal image of a road by adopting a ground penetrating radar, and performing disease identification on the acquired internal image of the road by utilizing the internal void disease identification convolutional neural network model of the road obtained in the step S1.1 to obtain an internal image of the damaged road;
s1.2.2, obtaining a disease road of an image in the disease road by adopting a drilling machine to drill step S1.2.1, and obtaining a road void area;
s1.2.3 the endoscope is put into the road void region, the top plate position and the bottom plate position of the road void region are determined by the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the height H of the road void region ha ；
S1.2.4 then filling water into the road void region obtained in step S1.2.2 until the water is filled, and recording the volume of the water filling as the volume V of the road void region a ；
S1.2.5 calculating the area S of the road void a The computational expression is:
s2, constructing an evaluation index of the service life of the interior of the road based on the parameters of the void area in the interior of the road obtained in the step S1;
the specific implementation method of the step S2 comprises the following steps:
S2.1, setting the total number of the road void areas as N T ；
S2.2, constructing an evaluation index of the service life of the interior of the road: the method comprises the steps of total number of road void areas, average height, average volume, average area, height change gradient, volume change gradient, quantity change gradient and area change gradient of the road void areas;
s2.2.1 average height of road void areaThe calculated expression of (2) is:
wherein the height of the kth void area of the road is
S2.2.2 average volume of road void areaThe calculated expression of (2) is:
wherein the volume of the kth void area of the road is
S2.2.3 average area of road void areaThe calculated expression of (2) is:
wherein the area of the kth void area of the road is
S2.2.4 gradient of altitude change in road voidThe calculated expression of (2) is:
wherein t is l 、t l+1 The first data acquisition time and the first (1) data acquisition time,for the average height of the road void area at the first data acquisition time, +.>The average height of the road void area at the 1+1st data acquisition time;
s2.2.5 gradient of volume change in road void regionThe calculated expression of (2) is:
wherein,for the average volume of the road void area at the first data acquisition time, +. >The average volume of the road void area for the 1+1st data acquisition time;
s2.2.6 number change gradient dN of road void areas T The calculated expression of (2) is:
wherein,track for the first data acquisition timeNumber of road void areas, +.>The number of road void areas for the 1+1st data acquisition time;
s2.2.7 gradient of area change in road void regionThe calculated expression of (2) is:
wherein,for the average area of the road void area at the first data acquisition time, +.>The average area of the road void area at the 1+1st data acquisition time;
s3, carrying out normalization processing on the road internal service life assessment index obtained in the step S2, and constructing a road internal service life comprehensive assessment index;
s4, calculating comprehensive evaluation indexes of the service life of the interior of the road according to the method of the step S3, and performing preliminary evaluation of the service life of the interior of the road;
s5, constructing a life distribution function model of the road section by adopting a three-parameter Weibull distribution function;
s6, optimizing the life distribution function model of the road section obtained in the step S5 to obtain an optimized life distribution function model of the road section, and calculating the average damage occurrence time of the road section;
S7, setting the average damage occurrence time, the road technical condition grade, the road width, the road length, the road materials, the road grade, the maintenance cost, the traffic volume and the road age of the road section obtained in the step S6 as road network maintenance factors, constructing a road network maintenance scoring matrix, and judging the road network maintenance priority;
the specific implementation method of the step S7 comprises the following steps:
s7.1, setting the average damage occurrence time, road technical condition grade, road width, road length, road material, road grade, maintenance cost, traffic volume and road age of the road section obtained in the step S6 as road network maintenance factors, wherein the number of the road network maintenance factors is 9;
s7.2, setting the number of roads in the road network as n, scoring road network maintenance factors of each road by adopting a manual assessment mode, constructing a road network maintenance scoring matrix PF, and calculating the expression as follows:
wherein pf is ij Scoring the ith road and the jth road network maintenance factors in the road network maintenance scoring matrix;
s7.3, carrying out standardized processing on indexes in all road network maintenance scoring matrixes obtained in the step S7.2, wherein the calculation expression is as follows:
Wherein,a standardized score for the ith road and the jth road network maintenance factors;
on the basis, a road network maintenance standardized scoring matrix PF is obtained b The computational expression is:
s7.4, constructing road network maintenance scoring entropy, wherein the calculation expression is as follows:
wherein PFS j Road network maintenance scoring entropy for the jth road network maintenance factors;
s7.5, constructing road network maintenance influence factors based on the road network maintenance scoring entropy obtained in the step S7.4, wherein the calculation expression is as follows:
wherein Fw j Road network maintenance influence factors for the jth road network maintenance factors;
s7.6, calculating a final score PFF corresponding to each road based on the road network maintenance influence factors obtained in the step S7.5, wherein the calculation expression is as follows:
PFF＝PF×Fw T
wherein Fw T And as the transposed matrix of Fw, arranging the final scores corresponding to the roads in the order from small to large, wherein the smaller the score is, the higher the maintenance priority is.
2. The optimized road network maintenance method according to claim 1, wherein the specific implementation method of step S3 comprises the following steps:
s3.1, dividing the road into NP sections by taking 50m as the road dividing length, wherein NP is the total number of road sections;
S3.2, taking a time point at which road construction starts to operate as a time zero point, respectively detecting a road void area of each section of road after the running time t of each section of road, and calculating a road internal service life evaluation index of each section of road void area;
s3.3, traversing step S3.2 to calculateObtaining the maximum value of the service life evaluation indexes in each road, including the maximum value of the total number of the road void areasAverage height maximum value of road void area +.>Average volume maximum>Average area maximum->Maximum value of the gradient of the height change->Maximum value of volume change gradient>Maximum value of the number change gradient>Maximum value of area change gradient->
S3.4, carrying out normalization processing on the road internal service life evaluation index of each road void area obtained in the step S3.2 by using the maximum value of the road internal service life evaluation index of each road void area obtained in the step S3.3 as a reference, wherein the calculation formula is as follows:
wherein,the average height of the road void area is normalized;
wherein,the total number of road void areas processed for normalization;
Wherein,an average volume of the road void area for normalization processing;
wherein,an average area of the road void area which is normalized;
wherein,high road void area for normalizationA gradient of degree variation;
wherein,a number change gradient of the road void area which is normalized;
wherein,a volume change gradient of a road void area which is normalized;
wherein,an area change gradient of a road void area which is normalized;
then, establishing a comprehensive evaluation index TK of the service life in the road, wherein the calculation expression is as follows:
wherein zk1 is a weight coefficient of an average height of the normalized road void areas, zk2 is a weight coefficient of a total number of the normalized road void areas, zk3 is a weight coefficient of an average volume of the normalized road void areas, zk4 is a weight coefficient of an average area of the normalized road void areas, zk5 is a weight coefficient of a height change gradient of the normalized road void areas, zk6 is a weight coefficient of a number change gradient of the normalized road void areas, zk7 is a weight coefficient of a volume change gradient of the normalized road void areas, zk8 is a weight coefficient of an area change gradient of the normalized road void areas;
S3.5, calculating comprehensive evaluation indexes of service lives of the f-th road section in the road at the time ti according to the method of the step S3.4
3. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of an optimized road network maintenance method according to any one of claims 1-2 when the computer program is executed.
4. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements an optimized road network maintenance method according to any of claims 1-2.
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