CN118428610A - Intelligent monitoring and evaluating method and system for highway tunnel - Google Patents

Abstract

The invention relates to the technical field of highway tunnel safety monitoring, in particular to an intelligent monitoring and evaluating method and system for a highway tunnel, wherein the method comprises the following steps: presetting a plurality of items to be monitored; setting a plurality of monitoring points on a highway tunnel target section, and acquiring safety state data of the highway tunnel target section based on each item to be monitored; preprocessing the safety state data of all monitoring points to obtain safety state input data corresponding to the items to be monitored; and (3) inputting the safety state input data corresponding to each item to be monitored, performing evaluation analysis by a fuzzy control method, and outputting a safety monitoring evaluation grade. The invention not only improves the accuracy and reliability of the data, provides comprehensive technical support for the maintenance and management of the highway tunnel, but also is beneficial to improving the operation efficiency and safety of the highway tunnel.

Description

Intelligent monitoring and evaluating method and system for highway tunnel

Technical Field

The invention relates to the technical field of highway tunnel safety monitoring, in particular to an intelligent monitoring and evaluating method and system for a highway tunnel.

Background

Road tunnels are an important component of modern transportation networks, and along with the increase of traffic volume and the extension of the service time of tunnels, the health condition of tunnel structures becomes an important factor affecting road safety and operation efficiency.

Currently, the performance evaluation of a highway tunnel structure mainly depends on manual inspection, periodic inspection and special inspection. Although these conventional methods can provide certain evaluation data, they require a lot of manpower to perform on-site inspections, especially for long tunnels and tunnels in remote areas, which are more costly. The manual inspection period is long, the data acquisition and analysis speed is low, and the health condition of the tunnel is difficult to reflect in time. The manual inspection result is greatly influenced by experience and subjective judgment of an inspector, and has larger uncertainty and error. The traditional inspection method is difficult to realize continuous monitoring of the state of the tunnel structure, and the problem of bursty structure cannot be captured in time.

Disclosure of Invention

In view of this, the invention provides a method and a system for intelligent monitoring and evaluating of a highway tunnel, which mainly aims to solve the problem of how to realize the rapidness, the intellectualization and the automation of the performance evaluation of a highway tunnel structure.

In one aspect, the invention provides a highway tunnel intelligent monitoring and evaluating method, which comprises the following steps:

Presetting a plurality of items to be monitored;

Setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on each item to be monitored;

Preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

and inputting the safety state input data corresponding to each item to be monitored, performing evaluation analysis by a fuzzy control method, and outputting a safety monitoring evaluation grade.

In some embodiments of the application, the plurality of items to be monitored include: lining stress, anchor rod axial force, surrounding rock pressure, vault settlement amount, surrounding rock deformation displacement amount and tunnel side wall vibration acceleration;

The safety state data is recorded as Ai, i=1, 2, …,6;

The safety state data Ai is a monitored value of the ith item to be monitored, and includes: lining stress monitoring value A1, anchor rod axial force monitoring value A2, surrounding rock pressure monitoring value A3, vault settlement monitoring value A4, surrounding rock deformation displacement monitoring value A5 and tunnel side wall vibration acceleration monitoring value A6.

In some embodiments of the application, the data preprocessing comprises: outlier removal and error correction.

In some embodiments of the present application, when the safety state data of the monitoring points are preprocessed to obtain the safety state input data corresponding to the item to be monitored, the method includes:

Abnormal value removal is carried out on the safety state data Ai of a plurality of monitoring points through the Z-Score, and a specific calculation formula is as follows:

；

wherein n is the nth monitoring point; The standard fraction of safety state data Ai of the nth monitoring point; Safety state data Ai for the nth monitoring point; the average value of the safety state data Ai of n monitoring points; standard deviation of safety state data Ai of n monitoring points;

presetting an abnormal value judgment threshold X, wherein X is more than 0;

When (when) And when the value is more than X, determining to reject the safety state data Ai of the nth monitoring point.

When (when)And when X is less than the value, determining to reserve safety state data Ai of the nth monitoring point.

In some embodiments of the present application, after the abnormal value removal of the safety state data Ai of the plurality of monitoring points by the Z-Score, the method further comprises:

Carrying out error correction on the safety state data Ai of a plurality of monitoring points with abnormal values removed through a polynomial regression model;

Based on the item to be monitored, a polynomial regression model of the monitoring component is obtained through calibration, and a specific calculation formula is as follows:

；

wherein, An error correction value of an nth monitoring point of the item to be monitored in the ith item;，，，…， And (5) the coefficients of the polynomial regression model of the item to be monitored in the ith term.

In some embodiments of the present application, after error correction is performed on the safety state data Ai of the plurality of monitoring points after the outlier removal by using a polynomial regression model, the method further includes:

Classifying the safety state data Ai of a plurality of monitoring points according to the items to be monitored to obtain a plurality of item data sets to be monitored;

The item data set to be monitored comprises: a lining stress monitoring value data set, an anchor rod axial force monitoring value data set, a surrounding rock pressure monitoring value data set, a vault settlement monitoring value data set, a surrounding rock deformation displacement monitoring value data set and a tunnel side wall vibration acceleration monitoring value data set;

And calculating the average value of the monitoring values in the data sets of the items to be monitored as safety state input data corresponding to the items to be monitored.

In some embodiments of the present application, the evaluation analysis is performed on the safety state input data corresponding to each item to be monitored by a fuzzy control method, and when the safety monitoring evaluation level is output, the method includes:

Selecting Gaussmf membership functions to divide high, medium and low intervals of safety state input data of each item to be monitored;

the specific calculation formula of Gaussmf membership functions is as follows:

；

wherein, Inputting data for the safety state of the ith item to be monitored; inputting the membership degree of the data for the safety state of the ith item to be monitored; a, b is Gaussmf membership function control parameters; e is the natural exponent base.

In some embodiments of the present application, the evaluation analysis is performed on the safety state input data corresponding to each item to be monitored by a fuzzy control method, and when the safety monitoring evaluation level is output, the method includes:

the method selects a bell Gbellmf membership function as an output function of a safety monitoring evaluation grade, and a specific calculation formula of the bell Gbellmf membership function is as follows:

；

wherein, Inputting the membership degree of the data for the safety state corresponding to each item to be monitored; k is the safety state input data corresponding to each item to be monitored; c, d is a control parameter of a Gbellmf membership function of the bell shape; e is the natural exponent base.

In some embodiments of the present application, after selecting the bell Gbellmf membership function as the output function of the security monitoring rating, the method further includes:

sequentially setting a first preset safety monitoring evaluation level, a second preset safety monitoring evaluation level and a third preset safety evaluation level from large to small; sequentially setting a first preset output function membership threshold and a second preset output function membership threshold from large to small;

When (when) When the output safety monitoring evaluation level is smaller than the membership threshold value of the second preset output function, determining that the output safety monitoring evaluation level is a third preset safety evaluation level;

When (when) Is smaller than a first preset output function membership threshold value andWhen the output function membership threshold value is greater than or equal to the second preset output function membership threshold value, determining that the output safety monitoring evaluation level is the second preset safety evaluation level;

When (when) And when the output function membership threshold value is greater than or equal to the first preset output function membership threshold value, determining the output safety monitoring evaluation level as the first preset safety evaluation level.

In another aspect, the present invention provides a highway tunnel intelligent monitoring and evaluating system, which comprises:

the monitoring acquisition unit is used for presetting a plurality of items to be monitored, setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on the items to be monitored;

The data processing unit is used for preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

And the result output unit is used for carrying out evaluation analysis on the safety state input data corresponding to each item to be monitored through a fuzzy control method and outputting a safety monitoring evaluation grade.

Compared with the prior art, the invention has the following beneficial effects: firstly, the abnormal value of the safety state data of the monitoring point is removed through the Z-Score, so that the accuracy and the reliability of the data are ensured, and a solid foundation is provided for subsequent analysis. And secondly, error correction is carried out on the data with the abnormal values removed by using a polynomial regression model, so that the accuracy of the data is further improved, and the actual safety state of the highway tunnel is more accurately reflected. And moreover, the safety state input data of each item to be monitored is evaluated and analyzed through a fuzzy control method, and the safety monitoring evaluation grade is output, so that the evaluation result is more objective and scientific, and powerful decision support is provided for maintenance and management of the highway tunnel. In addition, the invention also divides the safety state input data of each item to be monitored into high, medium and low regions by selecting Gaussmf membership functions, and selects the bell Gbellmf membership functions as the output functions of the safety monitoring evaluation grades, so that the evaluation process is more flexible and accurate, and the safety monitoring requirements under different conditions can be met. Meanwhile, by setting different safety monitoring evaluation grades and corresponding output function membership thresholds, the method can realize the fine management and early warning of the safety state of the highway tunnel, and improves the safety and reliability of operation of the highway tunnel. The intelligent monitoring and evaluating system and the intelligent monitoring and evaluating method for the highway tunnel not only improve the accuracy and the reliability of data, but also provide comprehensive technical support for the maintenance and the management of the highway tunnel through a scientific evaluation method and a flexible early warning mechanism, and are beneficial to improving the operation efficiency and the safety of the highway tunnel.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. In the drawings:

FIG. 1 is a flow chart of a highway tunnel intelligent monitoring and evaluating method provided by an embodiment of the invention;

fig. 2 is a functional block diagram of an intelligent monitoring and evaluating system for a highway tunnel according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in combination with the embodiment.

Referring to fig. 1, the present embodiment provides a method for intelligently monitoring and evaluating a highway tunnel, which includes:

s101: presetting a plurality of items to be monitored;

S102: setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on each item to be monitored;

S103: preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

S104: and inputting the safety state input data corresponding to each item to be monitored, performing evaluation analysis by a fuzzy control method, and outputting a safety monitoring evaluation grade.

It can be understood that in this embodiment, first, by presetting a plurality of items to be monitored and setting corresponding monitoring points and monitoring components on the target section of the highway tunnel, comprehensive and detailed monitoring on the safety state of the tunnel can be realized. This helps in time discover potential safety hazard, improves tunnel operation's security. And secondly, the safety state data acquired by the monitoring points are preprocessed, so that noise and abnormal values in the data can be eliminated, and the accuracy and reliability of the data are improved. This facilitates the accuracy and effectiveness of subsequent evaluation analyses. And moreover, the fuzzy control method is adopted to evaluate and analyze the safety state input data corresponding to each item to be monitored, so that the ambiguity and uncertainty of various factors can be fully considered, and the evaluation result is more in line with the actual situation. The method not only can output the security monitoring evaluation level, but also can provide detailed evaluation basis and suggestion, and provides powerful support for maintenance and management of tunnels. In addition, through a preset monitoring item and a monitoring component, automatic acquisition and processing of data can be realized; meanwhile, the application of the fuzzy control method also enables the evaluation and analysis process to be more intelligent, and reduces the interference of artificial factors.

In some embodiments of the application, the plurality of items to be monitored include: lining stress, anchor rod axial force, surrounding rock pressure, vault settlement amount, surrounding rock deformation displacement amount and tunnel side wall vibration acceleration;

The safety state data is recorded as Ai, i=1, 2, …,6;

The safety state data Ai is a monitored value of the ith item to be monitored, and includes: lining stress monitoring value A1, anchor rod axial force monitoring value A2, surrounding rock pressure monitoring value A3, vault settlement monitoring value A4, surrounding rock deformation displacement monitoring value A5 and tunnel side wall vibration acceleration monitoring value A6.

In some embodiments of the application, the data preprocessing comprises: outlier removal and error correction.

In some embodiments of the present application, when the safety state data of the monitoring points are preprocessed to obtain the safety state input data corresponding to the item to be monitored, the method includes:

Abnormal value removal is carried out on the safety state data Ai of a plurality of monitoring points through the Z-Score, and a specific calculation formula is as follows:

；

wherein n is the nth monitoring point; The standard fraction of safety state data Ai of the nth monitoring point; Safety state data Ai for the nth monitoring point; the average value of the safety state data Ai of n monitoring points; standard deviation of safety state data Ai of n monitoring points;

presetting an abnormal value judgment threshold X, wherein X is more than 0;

When (when) And when the value is more than X, determining to reject the safety state data Ai of the nth monitoring point.

When (when)And when X is less than the value, determining to reserve safety state data Ai of the nth monitoring point.

It can be understood that in this embodiment, by monitoring and comprehensively analyzing the safety status data of a plurality of items to be monitored in real time, the overall safety of the tunnel structure can be comprehensively estimated. Compared with the monitoring of a single item, the multi-dimensional and comprehensive monitoring mode is more accurate and reliable, and is beneficial to improving the accuracy of the tunnel structure safety pre-warning. Secondly, the method for removing the abnormal value and correcting the error is adopted in the data preprocessing stage, so that the influence of data noise and error on a safety evaluation result can be effectively reduced. By removing abnormal values and correcting errors, more accurate and reliable data input into the model can be ensured, and therefore the accuracy of safety early warning is improved.

In some embodiments of the present application, after the abnormal value removal of the safety state data Ai of the plurality of monitoring points by the Z-Score, the method further comprises:

Carrying out error correction on the safety state data Ai of a plurality of monitoring points with abnormal values removed through a polynomial regression model;

Based on the item to be monitored, a polynomial regression model of the monitoring component is obtained through calibration, and a specific calculation formula is as follows:

；

wherein, An error correction value of an nth monitoring point of the item to be monitored in the ith item;，，，…， And (5) the coefficients of the polynomial regression model of the item to be monitored in the ith term.

It can be understood that in this embodiment, a polynomial regression model is used to correct errors in the security state data after the outliers are removed. The polynomial regression model can capture the nonlinear relationship in the data and fit it accurately. By the method, errors can be further reduced, and accuracy and stability of data are improved. In addition, the coefficients of the polynomial regression model are obtained based on calibration data of the item to be monitored, and the model is customized and optimized according to the characteristics and requirements of the actual monitored item, so that the model can better adapt to the monitoring requirements of different items. The customized model not only improves the accuracy of monitoring, but also enhances the universality and flexibility of the model. According to the method and the device, through outlier removal and polynomial regression model error correction, accuracy and reliability of monitoring data are effectively improved, and more reliable analysis and decision basis are provided for monitoring projects. Meanwhile, the pertinence and the effectiveness of monitoring are further enhanced through a customized polynomial regression model, and remarkable beneficial effects are brought to monitoring work.

In some embodiments of the present application, after error correction is performed on the safety state data Ai of the plurality of monitoring points after the outlier removal by using a polynomial regression model, the method further includes:

Classifying the safety state data Ai of a plurality of monitoring points according to the items to be monitored to obtain a plurality of item data sets to be monitored;

The item data set to be monitored comprises: a lining stress monitoring value data set, an anchor rod axial force monitoring value data set, a surrounding rock pressure monitoring value data set, a vault settlement monitoring value data set, a surrounding rock deformation displacement monitoring value data set and a tunnel side wall vibration acceleration monitoring value data set;

And calculating the average value of the monitoring values in the data sets of the items to be monitored as safety state input data corresponding to the items to be monitored.

It can be understood that in this embodiment, by classifying the security status data of the monitoring points according to the items to be monitored, a plurality of sets of data of the items to be monitored are obtained, which is helpful for performing special analysis and comparison on different items, so that the security status of each item is more comprehensively known. And calculating the average value of the monitoring values in the data sets of the items to be monitored as the safety state input data corresponding to the items to be monitored, and providing powerful data support for subsequent evaluation and prediction. The overall safety state of each project can be more intuitively known through monitoring the value mean value, and corresponding countermeasures and preventive measures are formulated. According to the method, the accuracy and the reliability of the monitored data are improved through the methods of removing abnormal values, correcting errors, classifying the data and the like, effective data support is provided for subsequent safety evaluation and prediction, and the safety and the stability of projects are improved.

In some embodiments of the present application, the evaluation analysis is performed on the safety state input data corresponding to each item to be monitored by a fuzzy control method, and when the safety monitoring evaluation level is output, the method includes:

Selecting Gaussmf membership functions to divide high, medium and low intervals of safety state input data of each item to be monitored;

the specific calculation formula of Gaussmf membership functions is as follows:

；

wherein, Inputting data for the safety state of the ith item to be monitored; inputting the membership degree of the data for the safety state of the ith item to be monitored; a, b is Gaussmf membership function control parameters; e is the natural exponent base.

It can be understood that in this embodiment, by performing evaluation analysis on the safety state input data of the item to be monitored by using the fuzzy control method, effective processing of complex, inaccurate or fuzzy safety data can be achieved. In particular, by selecting Gaussmf membership functions, the high, medium, and low intervals of the security state input data of the item to be monitored can be more accurately divided. The Gaussmf membership function can flexibly adapt to the characteristics of different monitoring projects by adjusting the control parameters a and b, and the accuracy and reliability of an evaluation result are ensured. In addition, the specific calculation formula of Gaussmf membership functions can clearly describe the relationship between input data and membership, so that the evaluation process is more transparent and interpretable. By calculating the membership degree of the input data in each interval, a quantized evaluation result can be obtained, and objective evaluation and comparison of the safety state are facilitated. According to the embodiment, the fuzzy control method and Gaussmf membership function are adopted for safety monitoring and evaluation, so that effective processing and analysis of complex safety data can be realized, and the accuracy and reliability of safety monitoring are improved.

In some embodiments of the present application, the evaluation analysis is performed on the safety state input data corresponding to each item to be monitored by a fuzzy control method, and when the safety monitoring evaluation level is output, the method includes:

the method selects a bell Gbellmf membership function as an output function of a safety monitoring evaluation grade, and a specific calculation formula of the bell Gbellmf membership function is as follows:

；

wherein, Inputting the membership degree of the data for the safety state corresponding to each item to be monitored; k is the safety state input data corresponding to each item to be monitored; c, d is a control parameter of a Gbellmf membership function of the bell shape; e is the natural exponent base.

It can be understood that in this embodiment, by adopting the fuzzy control method to perform evaluation analysis on the safety state input data of each item to be monitored, and further output the safety monitoring evaluation level, the safety condition of the system can be reflected more accurately. Specifically, by selecting the bell-shaped Gbellmf membership function as the output function of the safety monitoring evaluation level, the characteristics of the function can be fully utilized, the input data can be effectively processed, and the safety monitoring evaluation level with a definite meaning can be output. The bell-shaped Gbellmf membership function has specific shape and property, and parameters c and d in a calculation formula can be adjusted according to actual conditions so as to realize flexible processing of different input data. By adjusting these parameters, the function can be made more sensitive to changes in the input data within a specific range, thereby reflecting the security state of the system more accurately. In addition, the bell-shaped Gbellmf membership function also has good mathematical property and calculation efficiency, so that the calculation complexity can be reduced and the processing speed can be improved while the evaluation accuracy is ensured. This is particularly important for real-time monitoring systems, and can ensure that the processing and analysis of a large amount of data are completed in a short time, and timely and accurate safety monitoring evaluation level information is provided for decision makers. According to the embodiment, the fuzzy control method and the bell-shaped Gbellmf membership function are adopted to output the safety monitoring evaluation level, so that more accurate and efficient safety monitoring evaluation can be realized, and powerful guarantee is provided for the safety operation of the system.

In some embodiments of the present application, after selecting the bell Gbellmf membership function as the output function of the security monitoring rating, the method further includes:

sequentially setting a first preset safety monitoring evaluation level, a second preset safety monitoring evaluation level and a third preset safety evaluation level from large to small; sequentially setting a first preset output function membership threshold and a second preset output function membership threshold from large to small;

When (when) When the output safety monitoring evaluation level is smaller than the membership threshold value of the second preset output function, determining that the output safety monitoring evaluation level is a third preset safety evaluation level;

When (when) Is smaller than a first preset output function membership threshold value andWhen the output function membership threshold value is greater than or equal to the second preset output function membership threshold value, determining that the output safety monitoring evaluation level is the second preset safety evaluation level;

When (when) And when the output function membership threshold value is greater than or equal to the first preset output function membership threshold value, determining the output safety monitoring evaluation level as the first preset safety evaluation level.

It can be understood that, in this embodiment, by setting different preset safety monitoring evaluation levels and corresponding preset output function membership thresholds, the actual condition of safety monitoring can be estimated more accurately. This hierarchical evaluation approach helps identify different levels of security risk, thereby developing more targeted security management measures. Secondly, by utilizing the characteristic of the membership function of the bell Gbellmf, namely that the function value gradually increases along with the increase of the input value and gradually decreases after reaching the peak value in a certain range, the output of the safety monitoring evaluation level can be ensured to be smoother and more continuous. The continuous evaluation mode is helpful to avoid mutation of the evaluation result, so that the evaluation result is more stable and reliable. Specifically, when the input value is smaller than the second preset output function membership threshold, the output safety monitoring evaluation level is a third preset safety evaluation level, which generally represents a higher safety risk. When the input value is between the first preset output function membership threshold and the second preset output function membership threshold, the output safety monitoring evaluation level is the second preset safety evaluation level. This means that there is a certain safety risk, but that it is relatively controllable. When the input value is greater than or equal to the first preset output function membership threshold, the output safety monitoring rating is the first preset safety rating, which generally represents a lower safety risk.

Referring to fig. 2, in another aspect, the present invention provides a highway tunnel intelligent monitoring and evaluating system, which includes:

the monitoring acquisition unit is used for presetting a plurality of items to be monitored, setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on the items to be monitored;

The data processing unit is used for preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

And the result output unit is used for carrying out evaluation analysis on the safety state input data corresponding to each item to be monitored through a fuzzy control method and outputting a safety monitoring evaluation grade.

It can be understood that in this embodiment, the monitoring collection unit presets a plurality of items to be monitored, and sets corresponding monitoring points on the target section of the highway tunnel, so that the whole coverage and real-time monitoring of the key parts and key parameters of the highway tunnel can be realized. The monitoring component can acquire the safety state data of the highway tunnel target section based on each item to be monitored, and provides accurate and reliable data support for subsequent data processing and result analysis. The data processing unit preprocesses the safety state data of all monitoring points, including data cleaning, format conversion, abnormal value processing and other operations, so as to obtain safety state input data corresponding to the items to be monitored. The step ensures the accuracy and consistency of the input data and improves the accuracy and reliability of the subsequent evaluation analysis. And the result output unit adopts a fuzzy control method to evaluate and analyze the safety state input data corresponding to each item to be monitored and output the safety monitoring evaluation grade. The fuzzy control method can fully consider the mutual influence and uncertainty among all factors and effectively comprehensively evaluate the safety state of the highway tunnel. Meanwhile, the output safety monitoring evaluation level can provide visual and clear reference information for management staff, and is beneficial to timely finding and processing potential safety hazards. Finally, the intelligent monitoring and evaluating system for the highway tunnel in the embodiment has the characteristics of automation and intellectualization, can monitor in real time, automatically analyze and respond quickly, and improves the efficiency and accuracy of safety management of the highway tunnel. Meanwhile, the system can be flexibly configured and expanded according to actual requirements, and the monitoring requirements of different highway tunnels are met.

It will be apparent to those skilled in the art that embodiments of the present application may provide a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (5)

1. The intelligent monitoring and evaluating method for the highway tunnel is characterized by comprising the following steps of:

Presetting a plurality of items to be monitored;

Setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on each item to be monitored;

Preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

The safety state input data corresponding to each item to be monitored is evaluated and analyzed through a fuzzy control method, and the safety monitoring evaluation grade is output;

The plurality of items to be monitored include: lining stress, anchor rod axial force, surrounding rock pressure, vault settlement amount, surrounding rock deformation displacement amount and tunnel side wall vibration acceleration;

The safety state data is recorded as Ai, i=1, 2, …,6;

The safety state data Ai is a monitored value of the ith item to be monitored, and includes: a lining stress monitoring value A1, an anchor rod axial force monitoring value A2, a surrounding rock pressure monitoring value A3, a vault settlement monitoring value A4, a surrounding rock deformation displacement monitoring value A5 and a tunnel side wall vibration acceleration monitoring value A6;

the data preprocessing comprises the following steps: outlier removal and error correction;

When the safety state data of a plurality of monitoring points are preprocessed to obtain the safety state input data corresponding to the item to be monitored, the method comprises the following steps:

Abnormal value removal is carried out on the safety state data Ai of a plurality of monitoring points through the Z-Score, and a specific calculation formula is as follows:

；

wherein n is the nth monitoring point; The standard fraction of safety state data Ai of the nth monitoring point; Safety state data Ai for the nth monitoring point; the average value of the safety state data Ai of n monitoring points; standard deviation of safety state data Ai of n monitoring points;

presetting an abnormal value judgment threshold X, wherein X is more than 0;

When (when) When the value is more than X, determining to reject the safety state data Ai of the nth monitoring point;

When (when) When the value is less than X, determining to reserve safety state data Ai of an nth monitoring point;

After the abnormal value removal is carried out on the safety state data Ai of the monitoring points through the Z-Score, the method further comprises the following steps:

Carrying out error correction on the safety state data Ai of a plurality of monitoring points with abnormal values removed through a polynomial regression model;

Based on the item to be monitored, a polynomial regression model of the monitoring component is obtained through calibration, and a specific calculation formula is as follows:

；

wherein, An error correction value of an nth monitoring point of the item to be monitored in the ith item;，，，…， Coefficients of the polynomial regression model for the item to be monitored of the ith term;

after error correction is carried out on the safety state data Ai of the monitoring points with the abnormal values removed through a polynomial regression model, the method further comprises the following steps:

Classifying the safety state data Ai of a plurality of monitoring points according to the items to be monitored to obtain a plurality of item data sets to be monitored;

The item data set to be monitored comprises: a lining stress monitoring value data set, an anchor rod axial force monitoring value data set, a surrounding rock pressure monitoring value data set, a vault settlement monitoring value data set, a surrounding rock deformation displacement monitoring value data set and a tunnel side wall vibration acceleration monitoring value data set;

And calculating the average value of the monitoring values in the data sets of the items to be monitored as safety state input data corresponding to the items to be monitored.

2. The intelligent monitoring and evaluating method for highway tunnel according to claim 1, wherein the step of evaluating and analyzing the safety state input data corresponding to each item to be monitored by a fuzzy control method and outputting the safety monitoring evaluation level comprises the following steps:

Selecting Gaussmf membership functions to divide high, medium and low intervals of safety state input data of each item to be monitored;

the specific calculation formula of Gaussmf membership functions is as follows:

；

wherein, Inputting data for the safety state of the ith item to be monitored; inputting the membership degree of the data for the safety state of the ith item to be monitored; a, b is Gaussmf membership function control parameters; e is the natural exponent base.

3. The intelligent monitoring and evaluating method for highway tunnel according to claim 2, wherein the step of evaluating and analyzing the safety state input data corresponding to each item to be monitored by a fuzzy control method and outputting the safety monitoring evaluation level comprises the following steps:

the method selects a bell Gbellmf membership function as an output function of a safety monitoring evaluation grade, and a specific calculation formula of the bell Gbellmf membership function is as follows:

；

wherein, Inputting the membership degree of the data for the safety state corresponding to each item to be monitored; k is the safety state input data corresponding to each item to be monitored; c, d is a control parameter of a Gbellmf membership function of the bell shape; e is the natural exponent base.

4. The intelligent monitoring and evaluating method for highway tunnel according to claim 3, further comprising, after selecting the bell-type Gbellmf membership function as the output function of the safety monitoring evaluation level:

sequentially setting a first preset safety monitoring evaluation level, a second preset safety monitoring evaluation level and a third preset safety evaluation level from large to small; sequentially setting a first preset output function membership threshold and a second preset output function membership threshold from large to small;

When (when) When the output safety monitoring evaluation level is smaller than the membership threshold value of the second preset output function, determining that the output safety monitoring evaluation level is a third preset safety evaluation level;

When (when) Is smaller than a first preset output function membership threshold value andWhen the output function membership threshold value is greater than or equal to the second preset output function membership threshold value, determining that the output safety monitoring evaluation level is the second preset safety evaluation level;

When (when) And when the output function membership threshold value is greater than or equal to the first preset output function membership threshold value, determining the output safety monitoring evaluation level as the first preset safety evaluation level.

5. A highway tunnel intelligent monitoring and evaluating system, characterized in that the highway tunnel intelligent monitoring and evaluating method according to any one of claims 1-4 is applied, comprising:

the monitoring acquisition unit is used for presetting a plurality of items to be monitored, setting a plurality of monitoring points on a highway tunnel target section, and setting monitoring components on the monitoring points, wherein the monitoring components are used for acquiring safety state data of the highway tunnel target section based on the items to be monitored;

The data processing unit is used for preprocessing the safety state data of all the monitoring points to obtain safety state input data corresponding to the item to be monitored;

And the result output unit is used for carrying out evaluation analysis on the safety state input data corresponding to each item to be monitored through a fuzzy control method and outputting a safety monitoring evaluation grade.