Bridge and tunnel prediction maintenance method

Abstract

The invention discloses a bridge and tunnel prediction maintenance method, and relates to the technical field of bridge and tunnel maintenance, and the method comprises the steps: determining a needed monitoring index according to the characteristics and operation demands of a bridge and tunnel, and then carrying out the real-time monitoring of a target bridge and tunnel through a monitoring sensing device in a data sensing unit, the monitoring sensing device comprises a temperature sensor, a humidity sensor and a displacement sensor; the method comprises the steps of constructing an intelligent bridge and tunnel prediction maintenance platform, obtaining a preset bridge and tunnel monitoring index set, monitoring a target bridge and tunnel, preprocessing a monitoring data set, encrypting and transmitting a standard bridge and tunnel monitoring data set, analyzing a bridge and tunnel health state, formulating a bridge and tunnel maintenance scheme, implementing bridge and tunnel maintenance and the like. The method realizes real-time monitoring, prediction and maintenance of the bridge and tunnel state, has the advantages of high efficiency, accuracy and safety, and carries out real-time monitoring and data analysis on the bridge and the tunnel by utilizing advanced technical means so as to predict and maintain the state of the bridge and the tunnel and ensure the safe operation of the bridge and the tunnel.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge and tunnel maintenance, and particularly relates to a bridge and tunnel predictive maintenance method. Background Art

[0002] Bridge and tunnel structures are the general terms for bridges, tunnels, culverts, open channels, overpasses, subways, overcrossing bridges, river regulation structures, etc. During the long-term use of bridges and tunnels, bridge collapse incidents often occur, resulting in serious casualties and economic losses.

[0003] How to strengthen the management and maintenance of bridges and tunnels throughout their life cycles and improve the service life of bridges lies in forming monitoring big data based on regular inspections and real-time data monitoring of bridges and tunnels, and on the basis of intelligent data analysis, implementing health maintenance on bridges in real time, so as to improve the service life of bridges, which has extremely important significance and urgency. For this reason, we provide a bridge and tunnel predictive maintenance method to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a bridge and tunnel predictive maintenance method, which includes steps such as constructing an intelligent bridge and tunnel predictive maintenance platform, obtaining a preset set of bridge and tunnel monitoring indicators, monitoring the target bridge and tunnel, preprocessing the monitoring data set, encrypting and transmitting the standard bridge and tunnel monitoring data set, analyzing the health status of the bridge and tunnel, formulating a bridge and tunnel maintenance plan, and implementing bridge and tunnel maintenance, etc., realizing real-time monitoring and predictive maintenance of the bridge and tunnel status.

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] The present invention is a bridge and tunnel predictive maintenance method, and the bridge and tunnel predictive maintenance method specifically includes:

[0007] S1: Construct an intelligent bridge and tunnel predictive maintenance platform;

[0008] S2: Obtain a preset set of bridge and tunnel monitoring indicators;

[0009] S3: Conduct real-time monitoring of the target bridge and tunnel;

[0010] S4: Preprocess the monitoring data set;

[0011] S5: Encrypt and transmit the standard bridge and tunnel monitoring data set;

[0012] S6: Analyze the health status of the bridge and tunnel according to the monitoring data set;

[0013] S7: Formulate a bridge and tunnel maintenance plan and maintain the bridge and tunnel according to the maintenance plan.

[0014] The present invention is further configured such that the construction of the intelligent bridge-tunnel prediction and maintenance platform specifically includes a data perception unit, a data transmission unit, a data analysis unit, and a maintenance decision-making unit;

[0015] Among them: the data perception unit is used to collect real-time monitoring data of the bridge-tunnel, the data transmission unit is used to transmit the data to the data analysis unit, the data analysis unit is used to process and analyze the data to evaluate the health status of the bridge-tunnel, and the maintenance decision-making unit is used to formulate a maintenance plan according to the analysis results.

[0016] The present invention is further configured such that the acquisition of the preset bridge-tunnel monitoring index set specifically includes determining the required monitoring indexes according to the characteristics and operation requirements of the bridge-tunnel. The monitoring indexes include the strength of the bridge-tunnel, the concrete carbonation depth, the steel bar position, and the cover thickness. These monitoring indexes will serve as the basis for subsequent monitoring and analysis.

[0017] The present invention is further configured such that the real-time monitoring of the target bridge-tunnel specifically includes using the monitoring and sensing devices in the data perception unit to conduct real-time monitoring of the target bridge-tunnel. The monitoring and sensing devices include temperature sensors, humidity sensors, and displacement sensors. During the monitoring process, it is necessary to ensure the accuracy and integrity of the data.

[0018] The present invention is further configured such that the preprocessing of the monitoring data set specifically includes:

[0019] S401: Filling the missing values in the data. Specifically, under the condition that the missing type is random missing, assuming that the model is correct for the complete samples, the unknown parameters can be estimated by maximum likelihood through the marginal distribution of the observed data, so as to fill the missing values in the data;

[0020] S402: Smoothing the noisy data. The locally weighted regression scatterplot smoothing method is used to process the data. Specifically, locally weighted scatterplot smoothing data, and the data obtained by using linear least squares and first-order polynomial fitting is used to replace the original data;

[0021] S403: Identifying or deleting outlier data by using the data jump method. Specifically, arranging the monitoring values from small to large, then the monitoring values containing gross errors must be distributed on both sides. There will be a jump phenomenon at the position where the gross error exists. The data is divided into two segments at the jump point. For the first time, the first jump point and its gentle monitoring values form a sequence of numbers. The Laida method is used to judge whether the jump point is a monitoring value containing gross error. If not, continue to use the same method for the next judgment until the monitoring value with the largest residual on this side is reached. Then all the other observed values on this side are observed values containing gross errors and should be excluded.

[0022] The present invention is further configured such that the encrypted transmission standard bridge and tunnel monitoring data set is specifically that the standard bridge and tunnel monitoring data set is encrypted and transmitted to the data analysis unit through the data transmission unit.

[0023] The present invention is further configured such that the analysis of the bridge and tunnel health status is specifically that in the data analysis unit, the standard bridge and tunnel monitoring data set is analyzed using the bridge and tunnel health analysis model, and the analysis results include the health assessment result and the life prediction value of the bridge and tunnel.

[0024] The present invention is further configured such that the formulation of the bridge and tunnel maintenance plan is specifically as follows:

[0025] S801: Input the bridge and tunnel health analysis result into the bridge and tunnel maintenance knowledge base in the maintenance decision-making unit;

[0026] S802: Use the maintenance decision-making analysis method in the knowledge base, combined with the actual situation of the bridge and tunnel, to formulate a reasonable maintenance plan.

[0027] The present invention has the following beneficial effects:

[0028] Through steps such as constructing an intelligent bridge and tunnel prediction and maintenance platform, obtaining a preset bridge and tunnel monitoring index set, monitoring the target bridge and tunnel, preprocessing the monitoring data set, encrypting and transmitting the standard bridge and tunnel monitoring data set, analyzing the bridge and tunnel health status, formulating a bridge and tunnel maintenance plan, and implementing bridge and tunnel maintenance, the present invention realizes real-time monitoring and predictive maintenance of the bridge and tunnel status. This method has the advantages of high efficiency, accuracy, and security, and uses advanced technical means to conduct real-time monitoring and data analysis on the bridge and tunnel to predict and maintain the status of the bridge and tunnel to ensure its safe operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments.

[0030] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0032] Please refer to Figure 1 , the present invention is a bridge and tunnel prediction and maintenance method, and the bridge and tunnel prediction and maintenance method specifically includes:

[0033] S1: Construct an intelligent bridge and tunnel prediction and maintenance platform;

[0034] S2: Obtain a preset bridge and tunnel monitoring index set;

[0035] S3: Conduct real-time monitoring of the target bridge and tunnel;

[0036] S4: Preprocess the monitoring data set;

[0037] S5: Encrypt and transmit the standard bridge and tunnel monitoring data set;

[0038] S6: Analyze the health status of the bridge and tunnel based on the monitoring data set;

[0039] S7: Formulate a maintenance plan for the bridge and tunnel and conduct maintenance on the bridge and tunnel according to the maintenance plan.

[0040] Specifically, constructing an intelligent bridge and tunnel predictive maintenance platform specifically includes a data perception unit, a data transmission unit, a data analysis unit, and a maintenance decision-making unit;

[0041] Among them: The data perception unit is used to collect the real-time monitoring data of the bridge and tunnel. It converts the state information of the bridge and tunnel into electrical signals or digital signals through various sensors and monitoring devices for subsequent processing and analysis;

[0042] The data transmission unit is used to transmit the data to the data analysis unit. During the transmission process, it is necessary to ensure the integrity and real-time nature of the data. Encryption transmission technology can ensure the security of the data during the transmission process;

[0043] The data analysis unit is used to process and analyze the data to evaluate the health status of the bridge and tunnel. It uses advanced algorithms and models to analyze and process the collected data to evaluate the health status of the bridge and tunnel and predict its lifespan. The analysis results will provide an important basis for subsequent maintenance decisions;

[0044] The maintenance decision-making unit is used to formulate a maintenance plan based on the analysis results. According to the results of the data analysis unit, combined with the actual situation and maintenance requirements of the bridge and tunnel, a reasonable maintenance plan is formulated. The maintenance plan will guide subsequent maintenance operations to ensure the safe operation of the bridge and tunnel.

[0045] It should be specifically explained that, specifically, obtaining the preset bridge and tunnel monitoring index set specifically includes determining the required monitoring indexes according to the characteristics and operation requirements of the bridge and tunnel. The monitoring indexes include the strength of the bridge and tunnel, the carbonation depth of concrete, the position of steel bars, and the cover thickness. These monitoring indexes will serve as the basis for subsequent monitoring and analysis. Through the predetermined monitoring indexes, the specific implementation of this method can be guaranteed.

[0046] Specifically, conducting real-time monitoring of the target bridge and tunnel specifically includes using the monitoring and sensing devices in the data perception unit to conduct real-time monitoring of the target bridge and tunnel. The monitoring and sensing devices include temperature sensors, humidity sensors, and displacement sensors. By conducting real-time monitoring of the data, the accuracy and integrity of the data can be guaranteed.

[0047] Specifically, the preprocessing of the monitoring data set specifically includes:

[0048] S401: Fill in the missing values in the data. Specifically, under the condition that the missing type is randomly missing, assuming that the model is correct for the complete samples, the unknown parameters can be estimated by the maximum likelihood method through the marginal distribution of the observed data, so as to fill in the missing values in the data.

[0049] S402: Smooth the noisy data. The locally weighted regression scatterplot smoothing method is used to process the data. Specifically, the locally weighted scatterplot is smoothed, and the data obtained by the linear least squares method and the first-order polynomial fitting is used to replace the original data.

[0050] S403: Use the data jump method to identify or delete the outlier data. Specifically, the monitoring values are arranged from small to large. Then, the monitoring values with gross errors must be distributed on both sides. There is a jump phenomenon at the position where the gross error exists. The data is divided into two segments at the jump point. For the first time, the first jump point and its gentle monitoring values form a sequence of numbers. The Laida method is used to determine whether the jump point is a monitoring value with a gross error. If not, continue to use the same method for the next judgment until the monitoring value with the largest residual on this side is obtained. Then, all other observed values on this side are observed values with gross errors and should be excluded.

[0051] It should be specifically noted that by preprocessing the collected bridge and tunnel monitoring data set, including filling in the missing values, smoothing the noisy data, identifying or deleting the outlier data, etc., and the preprocessed data set will be used as the standard bridge and tunnel monitoring data set for subsequent analysis.

[0052] Specifically, the encrypted transmission of the standard bridge and tunnel monitoring data set specifically means that the standard bridge and tunnel monitoring data set is encrypted and transmitted to the data analysis unit through the data transmission unit. The encryption process ensures the security and confidentiality of the data.

[0053] Specifically, analyzing the health status of the bridge and tunnel specifically means that in the data analysis unit, the standard bridge and tunnel monitoring data set is analyzed by using the bridge and tunnel health analysis model. The analysis results include the health assessment results and the life prediction values of the bridge and tunnel. By comparing the real-time data with the standard data, the specific status of the bridge and tunnel can be judged, thereby improving the accuracy of this method.

[0054] Specifically, formulating the bridge and tunnel maintenance plan specifically includes:

[0055] S801: Input the bridge and tunnel health analysis results into the bridge and tunnel maintenance knowledge base in the maintenance decision-making unit.

[0056] S802: Using the maintenance decision analysis method in the knowledge base, combined with the actual situation of the bridge and tunnel, formulate a reasonable maintenance plan. According to the formulated maintenance plan, carry out maintenance operations on the target bridge and tunnel. During the maintenance process, it is necessary to ensure the safety and effectiveness of the operations.

[0057] The control method used in the present invention is automatically controlled by a control unit. The control circuit of the control unit can be realized by simple programming by those skilled in the art and belongs to the common general knowledge in the art. Therefore, the control method and circuit connection are not explained in detail in the present invention.

[0058] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. The present specification selects and specifically describes these embodiments in order to better explain the principle and practical application of the present invention, so that those skilled in the art can well understand and utilize the present invention.

Claims

1. A bridge and tunnel predictive maintenance method, characterized in that: The bridge and tunnel predictive maintenance method specifically includes: S1: Build an intelligent bridge and tunnel predictive maintenance platform; S2: Obtain a preset bridge and tunnel monitoring indicator set; S3: Real-time monitoring of target bridges and tunnels; S4: Preprocess the monitoring data set; S5: Encrypted transmission of standard bridge and tunnel monitoring data set; S6: Analyze the health status of bridges and tunnels based on the monitoring data set; S7: Develop a maintenance plan for bridges and tunnels, and maintain bridges and tunnels according to the maintenance plan.

2. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The intelligent bridge and tunnel prediction maintenance platform specifically includes a data perception unit, a data transmission unit, a data analysis unit, and a maintenance decision unit; Among them: the data perception unit is used to collect real-time monitoring data of bridges and tunnels, the data transmission unit is used to transmit data to the data analysis unit, the data analysis unit is used to process and analyze the data to evaluate the health status of bridges and tunnels, and the maintenance decision unit is used to formulate a maintenance plan based on the analysis results.

3. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The obtaining of the preset bridge and tunnel monitoring indicator set specifically includes determining the required monitoring indicators according to the characteristics and operation requirements of the bridge and tunnel. The monitoring indicators include the strength of the bridge and tunnel, the carbonization depth of concrete, the position of steel bars, and the thickness of the protective layer. These monitoring indicators will serve as the basis for subsequent monitoring and analysis.

4. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The real-time monitoring of the target bridge and tunnel specifically includes using a monitoring sensor device in a data perception unit to monitor the target bridge and tunnel in real time, and the monitoring sensor device includes a temperature sensor, a humidity sensor, and a displacement sensor.

5. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The preprocessing of the monitoring data set specifically includes: S401: Filling missing values ​​in the data. Specifically, under the condition that the missing type is random missing, assuming that the model is correct for the complete sample, the unknown parameters can be estimated by the marginal distribution of the observed data, thereby filling the missing values ​​in the data; S402: Smoothing the noise data, using the local weighted regression scatter point smoothing method to process the data, specifically, using the local weighted scatter point smoothing data, using the linear least squares method and the first-order polynomial fitting to obtain the data to replace the original data; S403: Use the data jump method to identify or delete outlier data. Specifically, the monitoring values ​​are arranged from small to large, and the monitoring values ​​containing gross errors must be distributed on both sides. Jumps occur at the position where the gross errors exist. The data is divided into two sections at the jump point. The first jump point and its smooth monitoring value are combined into a set of series. The Laida method is used to determine whether the jump point is a monitoring value containing gross errors. If not, the same method is used to make the next judgment until this side has the monitoring value with the largest residual. In this case, all other observations on this side are observations containing gross errors and should be eliminated.

6. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The encrypted transmission of the standard bridge and tunnel monitoring data set specifically involves encrypting and transmitting the standard bridge and tunnel monitoring data set to the data analysis unit through the data transmission unit.

7. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The analysis of the health status of the bridge and tunnel is specifically that, in a data analysis unit, a bridge and tunnel health analysis model is used to analyze a standard bridge and tunnel monitoring data set, and the analysis result includes a health assessment result and a life prediction value of the bridge and tunnel.

8. A bridge and tunnel predictive maintenance method according to claim 1, characterized in that: The bridge and tunnel maintenance plan is specifically formulated as follows: S801: inputting the bridge and tunnel health analysis results into the bridge and tunnel maintenance knowledge base in the maintenance decision unit; S802: Utilize the maintenance decision analysis method in the knowledge base and combine it with the actual situation of the bridge and tunnel to formulate a reasonable maintenance plan.

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