Reservoir dam monitoring system and method based on flexible inclinometer

Abstract

The present invention relates to the technical field of remote monitoring of reservoir dams, and specifically to a reservoir dam monitoring system and method based on a flexible inclinometer. The method comprises: obtaining survey data of the reservoir dam at different collection points based on the flexible inclinometer, obtaining posterior samples of random field model parameters based on the survey data through Bayesian inversion analysis, performing reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtaining the stability reliability of the reservoir dam based on the posterior failure probability; obtaining a predicted value of reservoir dam settlement based on the survey data; obtaining the stability reliability of the reservoir dam and the predicted value of reservoir dam settlement, and generating an early warning signal based on the stability reliability and the predicted value of reservoir dam settlement. The reservoir dam monitoring system based on the flexible inclinometer of the present invention can achieve comprehensive monitoring of the entire reservoir dam area and improve the accuracy and reliability of monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring of reservoir dam bodies, and in particular to a reservoir dam body monitoring system and method based on a flexible inclinometer. Background Art

[0002] With the continuous advancement of infrastructure construction, large-scale engineering projects such as highways, railways, water conservancy dams, and mining are increasing in number. These projects are often accompanied by complex reservoir dams. The stability of reservoir dams is directly related to the overall safety and long-term operation of the project. However, due to the complex and changing geological conditions, the unpredictable natural environmental factors (such as rainfall and earthquakes), and the influence of human activities, reservoir dams are prone to deformation and instability during construction and operation, which can trigger natural disasters such as landslides and collapses, posing a significant threat to the safety of people's lives and property and the ecological environment.

[0003] Traditional reservoir dam monitoring methods, such as manual inspections and periodic measurements, suffer from long monitoring cycles, low data accuracy, and poor real-time performance, making them difficult to meet the high requirements for reservoir dam stability monitoring in modern projects. Therefore, it is particularly important to develop a high-precision, real-time, and wide-range reservoir dam monitoring system.

[0004] Therefore, there is an urgent need for a reservoir dam monitoring system and method based on a flexible inclinometer to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a reservoir dam monitoring system and method based on a flexible inclinometer to solve the technical problems of low accuracy, weak real-time performance and small monitoring range in existing solutions and traditional reservoir dam monitoring methods.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] On the one hand, the reservoir dam monitoring system based on the flexible inclinometer includes a data acquisition module, a reservoir dam stability reliability assessment module, a reservoir dam settlement value prediction module and an early warning monitoring module;

[0008] The data acquisition module is used to obtain the survey data of the reservoir dam body at different collection points based on the flexible inclinometer, wherein the survey data includes the displacement data of the reservoir dam body, the spatial position data of the collection points and the settlement data of the reservoir dam body;

[0009] The reservoir dam stability reliability assessment module is used to obtain posterior samples of random field model parameters based on survey data through Bayesian inversion analysis, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the reservoir dam stability reliability based on the posterior failure probability;

[0010] The reservoir dam settlement value prediction module is used to obtain the reservoir dam settlement prediction value based on the survey data;

[0011] The early warning monitoring module is used to obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

[0012] Furthermore, based on the survey data, a posterior sample of the random field model parameters is obtained through Bayesian inversion analysis. Based on the posterior sample, a reliability analysis is performed to calculate the posterior failure probability of the reservoir dam body. Specifically, the following process is included:

[0013] Let yj = {S1, S2...S j} is the survey data sample, where j is the number of collection points, S j It represents the displacement data of the reservoir dam body obtained at different collection points. Different collection points correspond to different depths and positions.

[0014] Update the random variable θ using Bayesian theory:

[0015] f(θ|y j )=K j f(θ)L(θ|y j );

[0016] Among them, f(θ|y j ) represents the survey data sample y j The posterior probability density function of θ provided; K j is a proportional coefficient independent of θ, and its specific value is set by the system; f(θ) represents the prior probability density function to quantify the prior information of θ; L(θ|y j ) is the likelihood function, indicating that y occurs when θ is given j possibility;

[0017]

[0018] in, cov J is the measurement error ε J The coefficient of variation, measurement error ε J Represents the survey data y J and The error, Represents the random variable representation corresponding to the spatial position data of the collection point, and the value of J is 1, 2...j;

[0019] Perform reliability analysis to calculate the posterior failure probability P(F|yj) of the reservoir dam:

[0020] P(F|yj)=∫I[G(θ)≤0]f(θ|yj)dθ;

[0021] Among them, G(θ) is the functional function for evaluating the safety status of the reservoir dam, and I[G(θ)≤0] means that when G(θ)≤0, I[G(θ)≤0] takes 1, otherwise I[G(θ)≤0] takes 0.

[0022] Furthermore, the stability reliability of the reservoir dam body is obtained based on the posterior failure probability, which specifically includes the following process:

[0023] An inverse proportional function is established between the posterior failure probability and the stability reliability of the reservoir dam:

[0024]

[0025] Among them, wd represents the stability reliability of the reservoir dam.

[0026] Furthermore, the predicted value of reservoir dam settlement based on the survey data specifically includes the following process:

[0027] Based on the reservoir dam settlement data, the soil composition of the reservoir dam, the proportion of the soil composition of the reservoir dam and the height d of the reservoir dam are obtained;

[0028] The maximum allowable settlement correction factor β is obtained based on the soil composition:

[0029] The weights corresponding to the soil components of the reservoir dam body are determined based on the soil composition of the reservoir dam body: the weights corresponding to clay, silty clay, fine sand and siltstone are K1, K2, K3 and K4 respectively, among which K4>K3>K2>K1;

[0030] Substitute the proportion and weight of the soil components of the reservoir dam into the correlation formula to calculate the maximum allowable settlement correction coefficient β. The correlation formula is as follows:

[0031] Among them, Af, As, Ad, and Ao are the component proportions corresponding to clay, silty clay, fine sand, and siltstone, respectively, and V is the sampling volume corresponding to the component proportions;

[0032] Based on the maximum allowable settlement correction coefficient β and the reservoir dam height d, the predicted value of reservoir dam settlement BPS is obtained:

[0033]

[0034] Where ρ is the settlement coefficient of the reservoir dam, ρ = 2×10 -3 , β is the slope of the reservoir dam.

[0035] Furthermore, generating an early warning signal based on the reservoir dam stability reliability and the reservoir dam settlement prediction value specifically includes the following process:

[0036] Substitute the reservoir dam stability reliability and the reservoir dam settlement prediction value into the correlation formula to calculate the reservoir dam monitoring risk index value LMS. The correlation formula is as follows:

[0037]

[0038] Among them, wd represents the stability reliability of the reservoir dam body, BPS represents the predicted settlement value of the reservoir dam body, α and γ are weight coefficients, which are 0.6 and 0.4 respectively;

[0039] Determine whether the reservoir dam monitoring risk index value LMS exceeds the preset threshold. If so, generate an early warning signal; if not, do not generate an early warning signal.

[0040] Furthermore, the flexible inclinometer used for collecting reservoir dam survey data adopts a segmented splicing installation structure.

[0041] Furthermore, the length of a single section of the flexible inclinometer used to collect reservoir dam survey data is 25cm / 50cm / 100cm.

[0042] On the other hand, the reservoir dam monitoring method based on the flexible inclinometer includes:

[0043] Using a flexible inclinometer to obtain survey data of the reservoir dam at different collection points, the survey data includes displacement data of the reservoir dam, spatial position data of the collection points, and settlement data of the reservoir dam;

[0044] Based on the survey data, a posterior sample of the random field model parameters is obtained through Bayesian inversion analysis. Based on the posterior sample, a reliability analysis is performed to calculate the posterior failure probability of the reservoir dam body, and the stability reliability of the reservoir dam body is obtained based on the posterior failure probability.

[0045] Obtain the predicted value of reservoir dam settlement based on survey data;

[0046] Obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

[0047] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0048] This flexible inclinometer-based reservoir dam monitoring system, through the rational arrangement of measurement points and an optimized data transmission network, enables comprehensive monitoring of the entire reservoir dam area, improving monitoring accuracy and reliability. Furthermore, it provides engineers with rich data support, helping them better understand the stability of the reservoir dam, develop scientific reinforcement and repair plans, and ensure the safety and stability of engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 This is a system block diagram of a reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention;

[0051] Figure 2 This is a workflow diagram of a reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention;

[0052] Figure 3 The present invention provides a flow chart of a reservoir dam monitoring method based on a flexible inclinometer. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0055] This embodiment provides a reservoir dam monitoring system based on a flexible inclinometer. Figure 1 : is a system block diagram of a reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention. Figure 1 As shown, the system includes a data acquisition module, a reservoir dam stability reliability assessment module, a reservoir dam settlement value prediction module and an early warning monitoring module;

[0056] The data acquisition module is used to obtain the survey data of the reservoir dam body at different collection points based on the flexible inclinometer, wherein the survey data includes the displacement data of the reservoir dam body, the spatial position data of the collection points and the settlement data of the reservoir dam body;

[0057] The reservoir dam stability reliability assessment module is used to obtain posterior samples of random field model parameters based on survey data through Bayesian inversion analysis, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the reservoir dam stability reliability based on the posterior failure probability;

[0058] The reservoir dam settlement value prediction module is used to obtain the reservoir dam settlement prediction value based on the survey data;

[0059] The early warning monitoring module is used to obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

[0060] In summary, the present invention uses a flexible inclinometer to obtain survey data of a reservoir dam at different collection points, wherein the survey data includes displacement data of the reservoir dam, spatial location data of the collection points, and reservoir dam settlement data; based on the survey data, a posterior sample of random field model parameters is obtained through Bayesian inversion analysis; reliability analysis is performed based on the posterior sample to calculate the posterior failure probability of the reservoir dam, and the stability reliability of the reservoir dam is obtained based on the posterior failure probability; a predicted value of the reservoir dam settlement is obtained based on the survey data; the reservoir dam stability reliability and the predicted value of the reservoir dam settlement are obtained, and an early warning signal is generated based on the reservoir dam stability reliability and the predicted value of the reservoir dam settlement. The reservoir dam monitoring system based on the flexible inclinometer of the present invention can achieve comprehensive monitoring of the entire reservoir dam area through the rational arrangement of measurement points and the optimization of the data transmission network, thereby improving the accuracy and reliability of monitoring. At the same time, the present invention can also provide rich data support for engineering personnel, helping them to better understand the stability of the reservoir dam, formulate scientific reinforcement and repair plans, and ensure the safety and stability of the engineering project.

[0061] It is worth noting that the data acquisition module, reservoir dam stability reliability assessment module, reservoir dam settlement value prediction module and early warning monitoring module can all achieve two-to-two communication and realize data transmission.

[0062] In some embodiments, obtaining a posterior sample of random field model parameters through Bayesian inversion analysis based on survey data, and performing reliability analysis based on the posterior sample to calculate the posterior failure probability of the reservoir dam specifically includes the following process:

[0063] Let yj = {S1, S2...S j} is the survey data sample, where j is the number of collection points, S j It represents the displacement data of the reservoir dam body obtained at different collection points. Different collection points correspond to different depths and positions.

[0064] Update the random variable θ using Bayesian theory:

[0065] f(θ|y j )=K j f(θ)L(θ|y j );

[0066] Among them, f(θ|y j ) represents the survey data sample y j The posterior probability density function of θ provided; K j is a proportional coefficient independent of θ, and its specific value is set by the system; f(θ) represents the prior probability density function to quantify the prior information of θ; L(θ|y j ) is the likelihood function, indicating that y occurs when θ is given j possibility;

[0067]

[0068] in, cov J is the measurement error ε J The coefficient of variation, measurement error ε J Represents the survey data y J and The error, Represents the random variable representation corresponding to the spatial position data of the collection point, and the value of J is 1, 2...j;

[0069] It is worth noting that the way a random variable is represented depends on whether it is discrete or continuous. Discrete random variables are represented by probability mass functions or distribution series, while continuous random variables are represented by probability density functions or cumulative distribution functions. These representations provide a complete description of the values ​​of the random variable and its probability distribution. is discrete.

[0070] Perform reliability analysis to calculate the posterior failure probability P(F|y j ):

[0071] P(F|yj)=∫I[G(θ)≤0]f(θ|yj)dθ;

[0072] Among them, G(θ) is the functional function for evaluating the safety status of the reservoir dam, and I[G(θ)≤0] means that when G(θ)≤0, I[G(θ)≤0] takes 1, otherwise I[G(θ)≤0] takes 0.

[0073] It is worth noting that Bayesian inversion analysis, within the framework of Bayesian statistics, uses Bayes' theorem and Bayesian probability for inference. It treats model parameters as random variables and obtains estimated values ​​of model parameters by calculating the posterior distribution based on known data and prior knowledge.

[0074] Furthermore, the stability reliability of the reservoir dam body is obtained based on the posterior failure probability, which specifically includes the following process:

[0075] An inverse proportional function is established between the posterior failure probability and the stability reliability of the reservoir dam:

[0076]

[0077] Where wd represents the stability reliability of the reservoir dam body. The lower the posterior failure probability, the higher the stability reliability of the reservoir dam body.

[0078] In some embodiments, Figure 2 This is a workflow diagram of a reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention. Figure 2 As shown in Figure 2, obtaining the predicted value of reservoir dam settlement based on survey data specifically includes the following steps:

[0079] Step S201: obtaining the soil composition of the reservoir dam body, the proportion of the soil composition of the reservoir dam body and the height d of the reservoir dam body based on the reservoir dam body settlement data;

[0080] Step S202: Obtain the maximum allowable settlement correction coefficient β based on the soil composition:

[0081] The weights corresponding to the soil components of the reservoir dam body are determined based on the soil composition of the reservoir dam body: the weights corresponding to clay, silty clay, fine sand and siltstone are K1, K2, K3 and K4 respectively, among which K4>K3>K2>K1;

[0082] It is worth noting that weights are usually used to measure the degree of influence of different factors or variables on a certain result or decision. The definition of weight is to assign a numerical value to each factor when comparing and evaluating multiple factors to reflect its importance or priority;

[0083] Substitute the proportion and weight of the soil components of the reservoir dam into the correlation formula to calculate the maximum allowable settlement correction coefficient β. The correlation formula is as follows:

[0084] Among them, Af, As, Ad, and Ao are the component proportions corresponding to clay, silty clay, fine sand, and siltstone, respectively, and V is the sampling volume corresponding to the component proportions;

[0085] Step S203: Obtain the predicted value of reservoir dam settlement BPS based on the maximum allowable settlement correction coefficient β and the reservoir dam height d:

[0086]

[0087] Where ρ is the settlement coefficient of the reservoir dam, ρ = 2×10 -3, β is the slope of the reservoir dam.

[0088] In some embodiments, generating an early warning signal based on the reservoir dam stability reliability and the reservoir dam settlement prediction value specifically includes the following process:

[0089] Substitute the reservoir dam stability reliability and the reservoir dam settlement prediction value into the correlation formula to calculate the reservoir dam monitoring risk index value LMS. The correlation formula is as follows:

[0090]

[0091] Among them, wd represents the stability reliability of the reservoir dam body, BPS represents the predicted settlement value of the reservoir dam body, α and γ are weight coefficients, which are 0.6 and 0.4 respectively;

[0092] Determine whether the reservoir dam monitoring risk index value LMS exceeds the preset threshold. If so, generate an early warning signal; if not, do not generate an early warning signal.

[0093] The early warning signal is used to alert project-related personnel to the stability status of the reservoir dam by issuing early warning information, and then formulate scientific reinforcement and repair plans to ensure the safety and stability of the project.

[0094] Once a warning signal is triggered, the system should immediately send the warning information to relevant personnel, including project managers, engineers, monitoring personnel, and emergency response teams, through appropriate means (such as text messages, phone calls, emails, and sirens). The warning information should clearly indicate the specific location of the reservoir dam, any abnormalities in the monitoring data, the possible risk level, and the recommended initial response measures.

[0095] Upon receiving the early warning information, relevant personnel should quickly activate the emergency response mechanism and organize professionals to conduct on-site inspections and assessments of the reservoir dam to further confirm the stability of the reservoir dam. At the same time, an emergency reinforcement and repair plan should be formulated and implemented to control further instability of the reservoir dam and reduce potential risks.

[0096] In some embodiments, a flexible inclinometer used to collect reservoir dam survey data adopts a segmented installation structure: The segmented installation structure of the flexible inclinometer means that its individual measurement units (or segments) can be independently manufactured and freely spliced ​​on-site as needed. This design brings the following advantages:

[0097] Flexibility: The inclinometer can be freely combined according to different well depths, eliminating the need to customize a specific length for each project.

[0098] Easy to handle: Each segment is relatively light, making it easy to transport and carry to the site, reducing the need for large machinery.

[0099] Easy maintenance: When a segment fails, only that segment needs to be replaced or repaired, without returning the entire inclinometer to the manufacturer, reducing maintenance costs and difficulty.

[0100] Reusable: After the project is completed, the inclinometer segments can be disassembled and reassembled for use in other projects, improving resource utilization.

[0101] In some embodiments, the length of a single section of the flexible inclinometer used to collect reservoir dam survey data is 25 cm / 50 cm / 100 cm.

[0102] This embodiment also provides a reservoir dam monitoring method based on a flexible inclinometer. Figure 3 This is a workflow diagram of a reservoir dam monitoring method based on a flexible inclinometer according to an embodiment of the present invention. Figure 3 As shown, the method includes:

[0103] Step S301: obtaining survey data of the reservoir dam body at different collection points using a flexible inclinometer, wherein the survey data includes displacement data of the reservoir dam body, spatial position data of the collection points, and settlement data of the reservoir dam body;

[0104] Step S302: Obtaining posterior samples of random field model parameters through Bayesian inversion analysis based on the survey data, performing reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam body, and obtaining the stability reliability of the reservoir dam body based on the posterior failure probability;

[0105] Step S303: obtaining a predicted value of reservoir dam settlement based on the survey data;

[0106] Step S304: Obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

[0107] In some embodiments, for survey data with a small amount of missing data and a short time interval, a linear interpolation method is used to fill in the missing data. The linear interpolation method is expressed as:

[0108]

[0109] Among them, the values ​​corresponding to the survey data at time k and time k+m are Q k and Q k+m , Q k+m is the missing data at the k+i moment.

[0110] Furthermore, the survey data is preprocessed:

[0111] Denoising: Survey data may contain various noises that can interfere with the authenticity and accuracy of the data. Therefore, appropriate denoising methods, such as windowed moving polynomial smoothing and wavelet denoising, are needed to eliminate unreal fluctuations and mutations in the data.

[0112] Data conversion: According to analysis needs, convert raw data into appropriate formats and units for subsequent processing and analysis.

[0113] Data standardization: In order to eliminate the impact of different dimensions and magnitudes on data analysis, the data needs to be standardized so that all data are at the same order of magnitude.

[0114] Quality check: Perform quality check on the preprocessed data to ensure its accuracy and reliability.

[0115] Error assessment: Evaluate the errors that may be introduced during data preprocessing and take appropriate measures to correct them.

[0116] Data storage: Store the pre-processed data in a secure and reliable database for subsequent query and analysis.

[0117] Data management: Establish a comprehensive data management system to ensure data security and confidentiality.

[0118] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can 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. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. 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 or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0119] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The reservoir dam monitoring system based on flexible inclinometer is characterized by: The system includes data acquisition module, reservoir dam stability reliability assessment module, reservoir dam settlement value prediction module and early warning monitoring module; The data acquisition module is used to obtain the survey data of the reservoir dam body at different collection points based on the flexible inclinometer, wherein the survey data includes the displacement data of the reservoir dam body, the spatial position data of the collection points and the settlement data of the reservoir dam body; The reservoir dam stability reliability assessment module is used to obtain posterior samples of random field model parameters based on survey data through Bayesian inversion analysis, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the reservoir dam stability reliability based on the posterior failure probability; The reservoir dam settlement value prediction module is used to obtain the reservoir dam settlement prediction value based on the survey data; The early warning monitoring module is used to obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement; Based on the survey data, Bayesian inversion analysis is performed to obtain posterior samples of random field model parameters. Based on the posterior samples, reliability analysis is performed to calculate the posterior failure probability of the reservoir dam body. The specific process includes the following: Let y j ={S1, S2...S j } is the survey data sample, where j is the number of collection points, S j It represents the displacement data of the reservoir dam body obtained at different collection points. Different collection points correspond to different depths and positions. Update the random variable θ using Bayesian theory: f(θ|y j )=K j f(θ)L(θ|y j ); Among them, f(θ|y j ) represents the survey data sample y j The posterior probability density function of θ provided; K j is a proportional coefficient independent of θ, and its specific value is set by the system; f(θ) represents the prior probability density function to quantify the prior information of θ; L(θ|y j ) is the likelihood function, indicating that y occurs when θ is given j possibility; in, cov J is the measurement error ε J The coefficient of variation, measurement error ε J Represents the survey data y J and The error, Represents the random variable representation corresponding to the spatial position data of the collection point, and the value of J is 1, 2...j; Perform reliability analysis to calculate the posterior failure probability P(F|y j ): P(F|y j )=∫I[G(θ)≤0]f(θ|y j )dθ; Among them, G(θ) is the functional function for evaluating the safety status of the reservoir dam body, and I[G(θ)≤0] means that when G(θ)≤, I[G(θ)≤0] takes 1, otherwise I[G(θ)≤0] takes 0.

2. The reservoir dam monitoring system based on the flexible inclinometer according to claim 1 is characterized in that: The specific process of obtaining the stability reliability of the reservoir dam body based on the posterior failure probability includes the following: An inverse proportional function is established between the posterior failure probability and the stability reliability of the reservoir dam: Among them, wd represents the stability reliability of the reservoir dam.

3. The reservoir dam monitoring system based on the flexible inclinometer according to claim 1 is characterized in that: Generate early warning signals based on the stability reliability of the reservoir dam and the predicted value of the reservoir dam settlement The following processes are included: Substitute the reservoir dam stability reliability and the reservoir dam settlement prediction value into the correlation formula to calculate the reservoir dam monitoring risk index value LMS. The correlation formula is as follows: Among them, wd represents the stability reliability of the reservoir dam body, BPS represents the predicted settlement value of the reservoir dam body, α and γ are weight coefficients, which are 0.6 and 0.4 respectively; Determine whether the reservoir dam monitoring risk index value LMS exceeds the preset threshold. If so, generate an early warning signal; if not, do not generate an early warning signal.

4. The reservoir dam monitoring system based on the flexible inclinometer according to claim 1 is characterized in that: The flexible inclinometer used to collect reservoir dam survey data adopts a segmented installation structure.

5. The reservoir dam monitoring system based on the flexible inclinometer according to claim 4 is characterized in that: The single section length of the flexible inclinometer used to collect reservoir dam survey data is 25cm / 50cm / 100cm.

6. A reservoir dam monitoring method based on a flexible inclinometer, applicable to a reservoir dam monitoring system based on a flexible inclinometer according to any one of claims 1 to 5, characterized in that: Methods include: Using a flexible inclinometer to obtain survey data of the reservoir dam at different collection points, the survey data includes displacement data of the reservoir dam, spatial position data of the collection points, and settlement data of the reservoir dam; Based on the survey data, a posterior sample of the random field model parameters is obtained through Bayesian inversion analysis. Based on the posterior sample, a reliability analysis is performed to calculate the posterior failure probability of the reservoir dam body, and the stability reliability of the reservoir dam body is obtained based on the posterior failure probability. Obtain the predicted value of reservoir dam settlement based on survey data; Obtain the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement, and generate an early warning signal based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

Images