CN115267143A - Road cavity defect detection system and detection method - Google Patents
Road cavity defect detection system and detection method Download PDFInfo
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Abstract
The invention discloses a road void defect detection system and a detection method, and particularly relates to the field of road detection, which comprises the following steps: determining a safety threshold of the cavity data of the monitored road region by using the average water content of the soil of the monitored road region and the average traffic flow, and acquiring the underground cavity data of the monitored road region to judge whether the underground cavity data has hidden danger defects according to the safety threshold of the cavity data; if the underground cavity data of the monitored road region is within the safety threshold, judging whether the cavity inducing factor of the monitored road region is within the safety threshold; if the current time is within the safety threshold, continuously monitoring; if the monitored road area underground cavity data is not in the safety threshold, determining the defect grade of the cavity and giving a prompt to alarm. Therefore, whether the induction factors have adverse effects on the underground cavities in the monitored road area or not is analyzed, and the accuracy and timeliness of the judgment and detection of the defects of the cavities are guaranteed.
Description
Technical Field
The invention relates to the technical field of road detection, in particular to a road void defect detection system and a detection method.
Background
The formation of urban road collapse has the characteristics of high concealment, strong burstiness, multiple inducements, great harmfulness and the like. The root cause of this is the heavy ground surface load, which causes the road bed to loosen and form road cavities. The buried depth and scale of the underground cavity are different due to different reasons for loosening the roadbed.
In the prior art, detection and evaluation of the defect degree of the underground cavity are rarely involved, and actually, the underground cavity with small scale and deep burial is not required to be treated because the underground cavity is difficult to repair and the damage to the road is not large, while the cavity with shallow burial and large scale needs to be treated in time, so that the damage caused by subsidence and collapse of the road is avoided. Therefore, the present invention proposes a solution to solve the above-mentioned problem, in terms of whether the determination and detection of the road void defect level are necessary.
Disclosure of Invention
In order to overcome the above defects in the prior art, embodiments of the present invention provide a road cavity defect detection system and a detection method, which monitor the state of an underground cavity in real time by using a ground penetrating radar, and analyze and early warn other factors inducing road collapse in real time according to the state of the underground cavity, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
the road void defect detection method comprises the following steps:
s101, determining a safety threshold of cavity data of a monitored road region by using the average soil moisture content and the average traffic flow of the monitored road region, collecting underground cavity data of the monitored road region, and judging whether the underground cavity data has hidden danger defects or not according to the safety threshold of the cavity data;
step S102, if the underground cavity data of the monitored road area is within a safety threshold, judging whether the cavity inducing factor of the monitored road area is within the safety threshold;
if the current time is within the safety threshold, the step S101 is skipped to for continuous monitoring;
if not, the underground cavity data is not in the safety threshold, and step S103 is performed;
and step S103, if the underground cavity data of the monitored road area is not within the safety threshold, determining the defect grade of the cavity and giving a prompt and alarm.
In a preferred embodiment, the factors inducing the cavity in the monitored road region comprise soil moisture content and vehicle load.
In a preferred embodiment, in step S102, a soil moisture content safety threshold and a vehicle load safety threshold of the monitored road area are respectively determined according to the actual cavity data of the monitored road area, and it is determined whether the soil moisture content and the vehicle load of the monitored road area exceed the safety thresholds.
In a preferred embodiment, the vehicle load monitoring is performed by calculating the traffic flow monitoring of the monitored road area, that is, different assignments are performed on various vehicle loads, and various vehicles passing through the monitored road area are counted, assigned and summed to obtain the total vehicle load of the monitored road.
In a preferred embodiment, in step S103, the void defect levels include a low-risk defect and a high-risk defect, and when the void data is not within the safety threshold, which is determined by step S101, the road void is monitored as the high-risk defect; and when the empty hole data is not in the safety threshold value, the step S102 determines that the influence of the vehicle load and the land moisture content exceeding the safety threshold value on the empty hole is subjected to overall risk level evaluation, and the defect level of the empty hole is determined according to the risk level evaluation.
In a preferred embodiment, the overall risk level assessment method for the influence of the vehicle load and the land moisture content exceeding the safety threshold on the cavity comprises the following specific steps:
the evaluation model constructed by the Logistic regression analysis method is used for carrying out overall risk level evaluation on the influence of the vehicle load and the land moisture content exceeding the safety threshold on the cavity, and the index equation of the Logistic is as follows:
wherein the content of the first and second substances,
in order to monitor the risk coefficient of the road on the cavity influence, Q is a constant term, namely the magnitude of the risk of the road on the cavity influence is monitored when all variables take the values of 0;
for different time periods for soil moisture content exceeding a safety threshold,
different time periods for which the traffic volume exceeds the safety threshold lasts;
and
are regression coefficients corresponding to the variables.
In a preferred embodiment, when the influence of the traffic flow and the land moisture content on the cavity is graded, 50 percent of the sample size is adopted, and the influence risk of the traffic flow and the land moisture content on the cavity is graded into 2 risk grades according to the risk;
when the hole defect grade with the risk value below 0.4999 belongs to a low grade, namely the hole is a low-risk defect;
when the risk value is above 0.4999, the hole defect grade belongs to high grade, that is, the hole is a high risk defect.
In a preferred embodiment, when judging whether the soil moisture content of the monitored road area exceeds the safety threshold, the soil moisture content is predicted according to the rainfall condition of the future monitored road area by the following specific method:
analyzing the future soil water content by adopting a BP neural network, wherein training samples adopted by the BP neural network are the weather forecast rainfall of a monitored road area and the monitored road soil water content at a corresponding moment, an input layer of the BP neural network is the weather forecast rainfall of the monitored road area, an output layer is the monitored road soil water content, and the soil water content of the monitored road area is obtained by inputting the weather forecast rainfall of the monitored road area after training.
The road cavity defect detection system is used for realizing the road cavity defect detection method, and comprises a server and an acquisition mechanism in communication connection with the server; the acquisition mechanism is used for acquiring data influencing road collapse; the server comprises a data processing center, a weather early warning module, a data analysis module, an early warning prompt module and a data storage module;
the weather early warning module is used for acquiring future rainfall data in the weather station network of the monitored road area;
the data processing center is used for correspondingly analyzing and converting the data acquired by the acquisition mechanism and the weather early warning module and carrying out standardized processing on the data;
the data analysis module is used for analyzing the data processed by the data processing center, determining the defect grade of the cavity and sending the analysis result to the early warning prompt module;
the early warning prompting module is used for prompting and alarming different defect grades of the cavity according to the defect grade of the cavity analyzed by the data analysis module;
and the data storage module is used for storing various data acquired and calculated by the server.
The road void defect detection system and the detection method have the technical effects and advantages that:
1. according to the method, the safety threshold of the cavity data of the monitored road region is determined by utilizing the average water content of the soil of the monitored road region and the average traffic flow, and the underground cavity data of the monitored road region is collected, so that the potential safety hazard of the monitored road region can be preliminarily judged;
2. according to the method and the device, the soil water content safety threshold and the traffic flow safety threshold of the monitored road area are respectively determined according to the actual cavity data of the monitored road area, and whether the soil water content and the traffic flow of the monitored road area exceed the safety thresholds is judged, so that whether the induction factors have adverse effects on the underground cavity of the monitored road area is analyzed through the safety thresholds of the induction factors of the underground cavity, the potential safety hazards of the underground cavity can be judged in real time, and the accuracy and the timeliness of judging and detecting the defects of the road cavity are guaranteed.
Drawings
FIG. 1 is a flow chart of a road void defect detection method according to the present invention;
FIG. 2 is a schematic structural diagram of a road void defect detection system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example 1
The road cavity defect detection system monitors the state of the underground cavity in real time through the ground penetrating radar, and carries out real-time analysis and early warning on other factors inducing the road collapse according to the state of the underground cavity, so that the road cavity detection effect can be better played, and the early warning is timely prompted on the road collapse.
Specifically, as shown in fig. 2, the system comprises a server and an acquisition mechanism in communication connection with the server; the acquisition mechanism is used for acquiring data influencing road collapse; the server comprises a data processing center, a weather early warning module, a data analysis module, an early warning prompt module and a data storage module.
The weather early warning module is used for acquiring future rainfall data in a weather station network of a monitored road area;
the data processing center is used for correspondingly analyzing and converting the data acquired by the acquisition mechanism and the weather early warning module and carrying out standardized processing on the data;
the data analysis module is used for analyzing the data processed by the data processing center, determining the defect grade of the cavity and sending the analysis result to the early warning prompt module;
the early warning prompting module is used for prompting and alarming different defect grades of the cavity according to the defect grade of the cavity analyzed by the data analysis module;
the data storage module is used for storing various data acquired and calculated by the server.
Among them, the root cause affecting the road collapse is the heavy earth surface load, which results in the road bed being loose and forming road cavities. Therefore, timely monitoring of the road cavity is necessary for early warning of road collapse, the ground penetrating radar is used for detecting the road cavity, and the inducing factor data of the monitored road to the road cavity, including vehicle action data, water action data and the like, are collected in real time.
Specifically, the acquisition mechanism comprises a ground penetrating radar, a traffic flow instrument and a soil moisture sensor.
The ground penetrating radar is used for quickly detecting the monitored road, acquiring cavity data below the monitored road and sending the basic data of the cavity to the data processing center. The method can detect whether a cavity exists in the ground bottom and the depth and the span of the cavity, and obtain the depth and the span information of the cavity of the monitored road. The ground penetrating radar is a nondestructive detecting instrument, which utilizes high-frequency electromagnetic waves in the form of short pulses of a broadband, sends electromagnetic signals to the ground through a transmitting antenna on the ground surface, returns to the ground after being reflected by a soil layer interface or an inhomogeneous body, receives the reflected signals of the electromagnetic waves through a receiving antenna, analyzes and explains the characteristic information of the soil layer or a target body through the time-frequency characteristics and the amplitude characteristics of the reflected signals of the electromagnetic waves, and can quantitatively calculate the size of an underground cavity.
The traffic flow instrument comprises a traffic flow counter camera and is used for acquiring the traffic flow information of the monitored road in real time and sending the acquired traffic flow information of the monitored road to the data processing center. The traffic flow instrument can judge the behavior of driving in/out of the area through the vehicle track on a monitored road, so that the quantity of various vehicles in and out of the area is counted, the traffic flow instrument can capture various vehicles passing through in unit time through the camera due to different loads of various vehicles, data are sent to the data processing center, different assignments are carried out on various vehicle loads through the data processing center, and the total vehicle load when various vehicles drive through the monitored road can be calculated and analyzed. And the size of the total vehicle load can be controlled by controlling the vehicle flow.
Specifically, the overall vehicle load of the monitored road may be represented by the following equation:
in the formula (I), the compound is shown in the specification,
in order to monitor the overall vehicle load of the road,
the number of the cars is the number of cars,
in order to be the number of the passenger cars,
the number of the trucks is the same as the number of the trucks,
、
、
the load coefficients of the automobile, the passenger car and the truck are respectively, so that the total vehicle load of the monitored road can be obtained by counting various traffic flow information of the monitored road section.
It should be noted that the present embodiment is only an example to classify the types of cars into cars, passenger cars and trucks, and actually, more detailed classification can be performed according to the requirements, so that the load calculation is more accurate.
The soil moisture sensor is used for collecting moisture content data of soil in a monitored road area and sending the data to the data processing center.
The data analysis module is mainly used for analyzing and monitoring the cavity state of the road according to the data processed by the data processing center. The factors influencing the road collapse are many, and the factors have mutual influence relation. The data analysis module analyzes the influence relation among the factors of the road collapse, so that the defect grade of the cavity is adjusted according to the safety threshold of each influence factor, and the early warning and monitoring of the safety state of the monitored road can be timely and accurately carried out.
It should be noted that, because the soil property correlation coefficients of the roads are different, the training sample of the neural network unit needs to select data for monitoring the road area. The method analyzes the influence relation among all factors by inputting the depth and the size of the cavity, the whole vehicle load and the land water content when the monitoring road historical road collapses.
The early warning prompting module is mainly used for receiving the defect grade of the cavity analyzed and calculated by the data analysis module and prompting and alarming the defect grade of the cavity.
The data storage module is mainly used for storing real-time data of all the influence factors and sending the data of all the influence factors to the data processing center when the road collapses, and the data analysis module can update the model of the data analysis module according to the data of all the influence factors when the road collapses, so that the safety threshold value for analyzing all the influence factors is more accurate.
Example 2
An embodiment 2 of the present invention introduces a road void defect detection method, as shown in fig. 1, which includes the following steps:
step S101, determining a safety threshold of the cavity data of the monitored road region by using the average water content and the average traffic flow of the soil of the monitored road region, and collecting the underground cavity data of the monitored road region to judge whether the underground cavity data has hidden danger defects or not.
If the monitored road region underground cavity data is not within the safety threshold, indicating that the cavity has a defect, and performing step S103; otherwise, the road is in a stable state, and the normal operation of the road is not influenced.
The hole data includes a depth of the hole and a span of the hole. The average water content of the soil can reflect the adhesion of the soil, namely the cohesiveness of the soil to a certain extent, the less the water content of the soil is, the stronger the cohesiveness is, namely the water content of the soil and the cohesiveness of the soil have certain correlation coefficients, the water content of the soil can be converted into the cohesiveness of the soil, and the correlation coefficients of different soil qualities are different, so that the coefficients are not limited; in order to ensure normal use of the monitored road in weekdays, the average traffic flow of the road is adopted to judge the defects of the underground cavities, and as described in embodiment 1, the average traffic flow can reflect the vehicle load borne by the road in a normal state through conversion. The server establishes a cavity analysis model through the data of the monitored road region so as to determine the safety threshold of the monitored road cavity.
Specifically, the method adopts FLAC3D software to perform modeling analysis on the cavities with different shapes and sizes of the monitored road, and determines the safety threshold of the scale (the depth and the span of the cavity) of the road under the average traffic flow, wherein the average traffic flow is converted into the land bearing load force, and the average water content of the soil is converted into the land adhesion force for modeling consideration. In addition, since the FLAC3D software modeling is adopted to analyze the road cavity risk in the prior art, and a person skilled in the art can understand the road cavity risk, the present invention is not described herein again. And respectively determining the depth initial safety threshold of the cavity and the span initial safety threshold of the cavity through control variables. When the initial safety threshold of the depth of the cavity is determined, the span of the cavity adopts a value which influences the road, and when the initial safety threshold of the span of the cavity is determined, the depth of the cavity adopts a value which influences the road. The value influencing the road means the value influencing the collapse of the road, that is, the value is not an absolute safety value, for example, when the initial safety threshold of the depth of the hole is determined, the span of the hole is 2 meters, and when the initial safety threshold of the span of the hole is determined, the depth of the hole is 1 meter. The specific value is determined according to the actually monitored road soil property state and the traffic flow. Meanwhile, when modeling analysis is carried out, the analysis time is the operation peak time of the monitored road because the analysis time cannot be permanent. Let t be, since the peak times of the roads are different, it is not particularly limited.
In this embodiment, the cavity safety threshold of the monitored road area is set as
I.e. by
Has two safety indexes of
And
wherein
A safety threshold expressed as a depth of the hole,
the safety threshold value is expressed as the span of the cavity, and the depth and the span of the cavity in the monitored road area are required to be respectively smaller than
And
if any one of the holes is greater than the corresponding safety threshold, the hole has a hidden danger, namely has a defect, and the road needs to be correspondingly maintained.
And S102, respectively determining a soil water content safety threshold and a traffic flow safety threshold of the monitored road area according to the actual cavity data of the monitored road area, and judging whether the soil water content and the traffic flow of the monitored road area exceed the safety thresholds or not.
If the safety threshold value is not exceeded, repeating the step S101 to carry out continuous monitoring;
if any item exceeds the safety threshold, step S103 is performed.
Specifically, the method still utilizes FLAC3D software to model the monitored road, and utilizes the land adhesion force (converted by soil moisture content), the land bearing load force (converted by vehicle flow) and the cavity data (substituting the actually collected cavity depth and span) for consideration. And considering that the space is within the safety threshold, so that the determined soil moisture content safety threshold and the traffic flow are larger than the values of the average soil moisture content and the average traffic flow of the road area. In addition, when the safety threshold of the soil moisture content is determined, in order to monitor the actual operation state of the road through load monitoring, the hole data is actually acquired data, and the load bearing capacity of the land is converted through the average traffic flow of the road; similarly, when the traffic flow safety threshold is determined, the cavity data is actually acquired data, and the land adhesion is converted from the average land moisture content of the road area. Meanwhile, when modeling analysis is carried out, the analysis time is the operation peak time of the monitored road because the analysis time cannot be permanent. Let t be, since the peak time of each road is different, it is not specifically limited herein.
This example sets the soil moisture content safety threshold to
Setting the traffic flow safety threshold value as
The two are used for monitoring and judging the traffic flow and the water content of the monitored road in real time, if any one exceeds a safety threshold, the situation that the road collapse is induced under the data of the road cavity is indicated, therefore, the cavity of the monitored road is no longer in a stable state, hidden dangers exist, namely, the road has cavity defects, and alarm prompt needs to be carried out on the situation, and corresponding maintenance operation needs to be carried out.
And S103, determining the defect grade of the cavity with the hidden danger on the monitored road, and performing corresponding prompt alarm.
Specifically, there are two types of hidden hole hazards existing in the underground monitored road, one is a hidden hole directly determined in step S101, and the other is a hole originally in a stable state and in a hidden state in step S102 due to the fact that the subsequent monitoring influence factor exceeds the safety threshold. Therefore, in step S103, both of them need to be considered.
The classification standard of the air hole defects is whether hidden dangers exist in normal use of subsequent roads, namely whether the roads sink to influence normal passing of subsequent traffic flow. Obviously, the hidden danger holes determined in step S101 all pass through the subsequent traffic normally, so the defect level is high, and corresponding processing is required immediately. In step S102, the void originally having a stable state is in a hidden danger state due to the fact that the subsequent monitoring influence factor exceeds the safety threshold, since the safety threshold is determined at the test time of the peak time of each road, and the monitoring of the traffic flow and the moisture content is usually real-time data, the duration of the monitoring needs to be further analyzed to determine whether a high risk is generated. The specific analysis method is as follows:
according to the method, an evaluation model constructed by a Logistic regression analysis method is adopted to carry out overall risk grade evaluation on the influence of the traffic flow and the water content exceeding the safety threshold value on the cavity. As described above, for the traffic flow and the water content exceeding the safety threshold, the duration is not easy, the risk effects are different, and the influence of the duration on the traffic flow and the water content is not linear, so the duration of the action effect is divided in the embodiment, and the duration of the traffic flow and the water content exceeding the safety threshold is estimated through analysis, so as to obtain the corresponding risk level.
The exponential equation of Logistic of the invention is as follows:
wherein the content of the first and second substances,
in order to monitor the risk coefficient of the influence of the duration of the road traffic flow and the soil moisture content on the cavity, Q is a constant term, namely the magnitude of the risk of the influence of the road on the cavity is monitored when all variables take values of 0;
for different time periods for soil moisture content exceeding a safety threshold,
different time periods for which the traffic flow exceeds the safety threshold lasts;
and with
Which are regression coefficients (i.e., weights) corresponding to the respective variables, respectively (the larger the coefficient, the greater the risk of void defects).
And with
Are respectively the soil contentWater rate and traffic flow at full analysis time.
It should be noted that the traffic flow and the duration of the soil moisture content can be only one condition in the present invention, and therefore,
and
only one item at most can be selected for calculation.
That is, if the traffic flow and the soil moisture content both exceed the safety threshold, the formula becomes:
wherein m is 1 or more and n or less.
And if the traffic flow and the soil moisture content do not exceed the safety threshold, the formula is changed correspondingly.
According to the formula, the risk coefficients of the influence on the cavity caused by different durations of the traffic flow and the water content exceeding the safety threshold can be calculated, and further, the invention also comprises the steps of grading each risk coefficient, wherein the steps are shown in the following table:
when the influence of the traffic flow and the water content of the land on the cavity is graded, 50 percent of sample size is adopted, and the influence risk of the traffic flow and the water content of the land on the cavity is graded into 2 risk levels according to the risk. When the void defect level with the risk value below 0.4999 is low, namely the influence of the traffic flow and the soil water content exceeding the safety threshold on the road is small, the void defect level does not need to be subjected to similar management such as closed maintenance immediately at the moment, and when the void defect level with the risk value above 0.4999 is high, key marks are needed and relevant maintenance processing is immediately carried out to avoid the subsequent sinking and collapsing of the road.
Specifically, the risk grade evaluation quantification model for grade division of the cavity defect comprises four logical factors: the method comprises the following steps of firstly, indexes, namely risk factors (the embodiment refers to different durations of vehicle flow and soil moisture content) influencing equipment operation and maintenance dangerousness; the weights of the indexes, namely the proportion of the duration of each traffic flow and the soil water content in the comprehensive evaluation of the risk of the void defects; thirdly, an operational equation, namely a risk result is obtained through what mathematical operation process; and fourthly, obtaining risk results, namely the indexes with the respective weights are obtained by operation of an operational equation. The step of constructing the hole defect overall risk level evaluation quantification model comprises four steps: firstly, carrying out data conversion and processing on various evaluation factors collected in a sample, and converting the evaluation factors into a data language which can be identified by computer software; secondly, performing Logistic regression analysis on the evaluation factors by using SPSS software, and screening out factors which have important correlation with the result and the weight thereof; thirdly, substituting the evaluation factors and the weights into a Logistic regression equation to carry out operation, thereby obtaining a result; and finally, marking a risk grade according to actual needs and judging the defect grade of the product.
In the data conversion and processing of various evaluation factors collected in a sample, the evaluation factors are converted into a data language which can be identified by computer software, and the factors with important correlation and the weights thereof are screened out by SPSS software, the sample can be automatically selected and adjusted according to actual conditions, for example, the sample can be: the influence condition of the historical traffic flow of the road similar to the monitored road and the duration of the soil moisture content exists, the influence condition of the historical traffic flow of the monitored road and the duration of the soil moisture content exists, and the like; according to different sample selections, the weights of all indexes (different durations of the traffic flow and the soil moisture content) determined by SPSS software are different, and the Q values when all index variables take values of 0 are also different, and the weights of all indexes are used
And
the risk factors influencing the operation and maintenance risks of the equipment are respectively expressed
And
and (4) showing.
Example 3
The difference between the embodiment 3 of the present invention and the above embodiments is that, in the above embodiments, the soil moisture sensor is used to monitor the road in real time for the collection of the soil moisture content, but the soil moisture content in a future period cannot be effectively predicted, which results in poor early warning effect. The embodiment analyzes the soil moisture content for the rainfall condition through the weather early warning module. Specifically, the method adopts a BP neural network to analyze the future soil moisture content, wherein samples adopted by the BP neural network are the weather forecast rainfall of a monitored road region and the monitored road soil moisture content at corresponding moments, an input layer of the BP neural network is the weather forecast rainfall of the monitored road region, an output layer is the monitored road soil moisture content, and therefore the relation between the weather forecast rainfall and the soil moisture content can be searched through the historical weather forecast rainfall and the soil moisture content of the monitored road region, weather forecast rainfall information can be input through the BP neural network, the future soil moisture content of the monitored road can be obtained, and prompt and early warning can be carried out in advance.
Meanwhile, in order to early warn the traffic flow of the monitored road section in advance, the traffic flow meters can be arranged at the two ends of the monitored road so as to carry out statistical prediction on the overall traffic flow change of the road, and further, in order to better determine the influence of the vehicle load on the road, the traffic flow meters are arranged at the two ends of the road cavity after the position of the road cavity is determined, so that the traffic flow passing through the road section above the cavity can be more accurately mastered.
It should be noted that, the calculation of the magnitude and duration of the traffic flow passing through the road by monitoring and collecting the traffic flow information at the two ends of the road is known to those skilled in the art and will not be described herein again.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, data center, etc., that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (9)
1. The road void defect detection method is characterized by comprising the following steps:
s101, determining a safety threshold of cavity data of a monitored road region by using the average soil moisture content and the average traffic flow of the monitored road region, collecting underground cavity data of the monitored road region, and judging whether the underground cavity data has hidden danger defects or not according to the safety threshold of the cavity data;
step S102, if the underground cavity data of the monitored road area is within a safety threshold, judging whether a cavity inducing factor of the monitored road area is within the safety threshold;
if the current value is within the safety threshold value, the step S101 is skipped back to for continuous monitoring;
if the data is not in the safety threshold, the underground cavity data is not in the safety threshold at the moment, and the step S103 is carried out;
and step S103, if the underground cavity data of the monitored road area is not within the safety threshold, determining the defect grade of the cavity and giving a prompt and alarm.
2. The road void defect detection method according to claim 1, characterized in that: the monitoring road area cavity inducing factors comprise soil moisture content and vehicle load.
3. The road void defect detection method according to claim 2, characterized in that: in step S102, a soil moisture content safety threshold and a vehicle load safety threshold of the monitored road area are respectively determined according to the actual cavity data of the monitored road area, and it is determined whether the soil moisture content and the vehicle load of the monitored road area exceed the safety thresholds.
4. The road void defect detection method according to claim 3, characterized in that: the vehicle load monitoring is carried out by calculating the vehicle flow monitoring of the monitored road area, namely different assignments are carried out on various vehicle loads, various vehicles passing the monitored road area are counted, and the total vehicle load of the monitored road is obtained by assigning and summing the various vehicles.
5. The road hole defect detection method according to claim 4, characterized in that: in step S103, the void defect levels include a low-risk defect and a high-risk defect, and when the void data is not within the safety threshold value, which is determined by step S101, the monitored road void is a high-risk defect; and when the void data is not in the safety threshold value, the step S102 determines that the vehicle load and the land moisture content exceed the safety threshold value, so that the influence of the void is subjected to overall risk level evaluation, and the defect level of the void is determined according to the risk level evaluation.
6. The road void defect detection method according to claim 5, characterized in that: the specific method for evaluating the overall risk level of the influence of the vehicle load and the land moisture content exceeding the safety threshold on the cavity comprises the following steps:
the method comprises the following steps of carrying out overall risk grade evaluation on the influence of the vehicle load and the land moisture content exceeding a safety threshold value on a cavity by adopting an evaluation model constructed by a Logistic regression analysis method, wherein an exponential equation of the Logistic is as follows:
wherein, the first and the second end of the pipe are connected with each other,
in order to monitor the risk coefficient of the road on the cavity influence, Q is a constant term, namely the magnitude of the risk of the road on the cavity influence is monitored when all variables take the values of 0;
for different time periods for soil moisture content exceeding a safety threshold,
different time periods for which the traffic volume exceeds the safety threshold lasts;
and
are regression coefficients corresponding to the variables.
7. The road void defect detection method according to claim 6, characterized in that: when the influence of the traffic flow and the land water content on the cavity is graded, adopting 50 percent of the sample size to grade the influence risk of the traffic flow and the land water content on the cavity into 2 risk grades according to the risk;
when the hole defect grade with the risk value below 0.4999 belongs to a low grade, namely the hole is a low-risk defect;
when the risk value is above 0.4999, the hole defect grade belongs to high grade, that is, the hole is a high risk defect.
8. The road void defect detection method according to claim 3, characterized in that: when judging whether the soil moisture content of the monitored road region exceeds a safety threshold value, predicting the soil moisture content according to the rainfall condition of the future monitored road region, wherein the specific method comprises the following steps:
analyzing the future soil water content by adopting a BP neural network, wherein training samples adopted by the BP neural network are the weather forecast rainfall of a monitored road area and the monitored road soil water content at a corresponding moment, an input layer of the BP neural network is the weather forecast rainfall of the monitored road area, an output layer is the monitored road soil water content, and the soil water content of the monitored road area is obtained by inputting the weather forecast rainfall of the monitored road area after training.
9. A road void defect detection system for implementing the road void defect detection method of any of claims 1-8, characterized in that: comprises a server and an acquisition mechanism in communication connection with the server; the acquisition mechanism is used for acquiring data influencing road collapse; the server comprises a data processing center, a weather early warning module, a data analysis module, an early warning prompt module and a data storage module;
the weather early warning module is used for acquiring future rainfall data in the weather station network of the monitored road area;
the data processing center is used for correspondingly analyzing and converting the data acquired by the acquisition mechanism and the weather early warning module and carrying out standardized processing on the data;
the data analysis module is used for analyzing the data processed by the data processing center, determining the defect grade of the cavity and sending the analysis result to the early warning prompt module;
the early warning prompting module is used for prompting and warning different defect grades of the cavity according to the defect grade of the cavity analyzed by the data analysis module;
and the data storage module is used for storing various data acquired and calculated by the server.
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