Ultra-long-distance reservoir dam body monitoring system and method based on flexible inclinometer

Abstract

The invention relates to the technical field of reservoir dam body remote monitoring, in particular to an ultra-long-distance reservoir dam body monitoring system and method based on a flexible inclinometer. The method comprises the following steps: acquiring survey data of a reservoir dam body at different acquisition points based on a flexible inclinometer, acquiring a posterior sample of random field model parameters through Bayesian inversion analysis based on the survey data, and executing reliability analysis based on the posterior sample to calculate a posterior failure probability of the reservoir dam body. The stability reliability of the reservoir dam body is obtained based on the posterior failure probability; obtaining a reservoir dam settlement prediction value based on the survey data; and obtaining the stability reliability of the reservoir dam body and a reservoir dam body settlement prediction value, and generating an early warning signal based on the stability reliability of the reservoir dam body and the reservoir dam body settlement prediction value. According to the ultra-long-distance reservoir dam body monitoring system based on the flexible inclinometer, comprehensive monitoring of the whole reservoir dam body area can be achieved, and the monitoring accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of remote monitoring of reservoir dam bodies, and in particular to an ultra-long distance 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, and these projects are often accompanied by complex reservoir dams. The stability of the reservoir dam is directly related to the overall safety and long-term operation of the project. However, due to the complex and changeable geological conditions, the unpredictability of natural environmental factors (such as rainfall, earthquakes, etc.), and the influence of human activities, the reservoir dam is very prone to deformation and instability during construction and operation, thus causing natural disasters such as landslides and collapses, posing a major 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 regular measurements, have disadvantages such as long monitoring cycles, low data accuracy, and poor real-time performance, which are difficult to meet the high requirements of modern engineering for reservoir dam stability monitoring. Therefore, it is particularly important to develop a reservoir dam monitoring system with high accuracy, strong real-time performance, and a wide monitoring range.

[0004] Therefore, there is an urgent need for an ultra-long distance 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 an ultra-long distance 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 the 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 ultra-long distance reservoir dam monitoring system based on flexible inclinometer includes data acquisition module, reservoir dam stability reliability assessment module, reservoir dam settlement value prediction module and early warning monitoring module;

[0008] The data acquisition module is used to acquire 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 the posterior samples of the random field model parameters through Bayesian inversion analysis based on the survey data, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the stability reliability of the reservoir dam 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, reliability analysis is performed to calculate the posterior failure probability of the reservoir dam body, which specifically includes the following process:

[0013] Let y j ={S1, S2...S j} is the survey data sample, where j is the number of collection points, S j It indicates 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 is provided; Kj 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|y j ):

[0020] P(F|yj )=∫I[G(θ)≤0]f(θ|y j )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] is 1, otherwise I[G(θ)≤0] is 0.

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

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

[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 processes:

[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 components 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 composition of the reservoir dam into the correlation formula to calculate the maximum allowable settlement correction coefficient β, which 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 stability reliability of the reservoir dam and the predicted value of the reservoir dam settlement specifically includes the following processes:

[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, BPS represents the predicted settlement value of the reservoir dam, 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 a warning signal; if not, do not generate a warning signal.

[0040] Furthermore, the flexible inclinometer used for collecting reservoir dam body survey data adopts a segmented 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 ultra-long distance reservoir dam monitoring method based on flexible inclinometer includes:

[0043] The survey data of the reservoir dam at different collection points are obtained based on the flexible inclinometer, wherein the survey data includes the displacement data of the reservoir dam, the spatial position data of the collection points and the 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, and reliability analysis is performed based on the posterior sample 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] Based on the survey data, the predicted value of reservoir dam settlement is obtained;

[0046] The stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement are obtained, and an early warning signal is generated 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] The ultra-long-distance reservoir dam monitoring system based on the flexible inclinometer of the present invention can realize comprehensive monitoring of the entire reservoir dam area by reasonably arranging measuring points and optimizing 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 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 drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

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

[0052] Figure 3 The present invention is a flowchart of a method for monitoring an ultra-long distance reservoir dam based on a flexible inclinometer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0054] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. 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 may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may 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 an ultra-long distance reservoir dam monitoring system based on a flexible inclinometer. Figure 1is a system block diagram of an ultra-long distance reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention, such as 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 acquire 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 the posterior samples of the random field model parameters through Bayesian inversion analysis based on the survey data, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the stability reliability of the reservoir dam 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 obtains 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; based on the survey data, the posterior samples of the random field model parameters are obtained by Bayesian inversion analysis, reliability analysis is performed based on the posterior samples 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; the predicted value of the settlement of the reservoir dam body is obtained based on the survey data; the stability reliability of the reservoir dam body and the predicted value of the settlement of the reservoir dam body are obtained, and an early warning signal is generated based on the stability reliability of the reservoir dam body and the predicted value of the settlement of the reservoir dam body. The ultra-long-distance reservoir dam monitoring system based on the flexible inclinometer of the present invention can realize comprehensive monitoring of the entire reservoir dam body area and improve the accuracy and reliability of monitoring by reasonably arranging the measuring points and optimizing the data transmission network. 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 body, formulate scientific reinforcement and repair plans, and ensure the safety and stability of engineering projects.

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

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

[0063] Let y j ={S1, S2...S j} is the survey data sample, where j is the number of collection points, S j It indicates 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 is provided; Kj 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|y j )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] is 1, otherwise I[G(θ)≤0] is 0.

[0073] It is worth noting that Bayesian inversion analysis uses Bayesian theorem and Bayesian probability for inference within the framework of Bayesian statistics. It regards 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 processes:

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

[0076]

[0077] Among them, 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 : is a workflow diagram of an ultra-long distance reservoir dam monitoring system based on a flexible inclinometer according to an embodiment of the present invention, such as Figure 2 As shown in the figure, 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 components 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 composition of the reservoir dam into the correlation formula to calculate the maximum allowable settlement correction coefficient β, which 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 stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement 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, BPS represents the predicted settlement value of the reservoir dam, 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 a warning signal; if not, do not generate a 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 the 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, in an appropriate manner (such as text messages, phone calls, emails, alarms, etc.). The warning information should clearly indicate the specific location of the reservoir dam, the abnormality of the monitoring data, the possible risk level, and the recommended initial response measures.

[0095] After receiving the early warning information, relevant personnel should quickly activate the emergency response mechanism and organize professionals to conduct on-site inspection and assessment 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 the further instability of the reservoir dam and reduce potential risks.

[0096] In some embodiments, the flexible inclinometer used to collect reservoir dam survey data adopts a segmented installation structure: The flexible inclinometer adopts a segmented installation structure, which means that each measurement unit (or segment) can be independently manufactured and freely spliced ​​on site as needed. This design brings the following conveniences:

[0097] Flexibility: Ability to freely combine according to different inclination well depths, without the need to customize inclinometers of specific length for each project.

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

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

[0100] Reusable: After the project is completed, the inclinometer segments can be disassembled and reassembled for 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 method for monitoring a reservoir dam over an ultra-long distance based on a flexible inclinometer. Figure 3 is a workflow diagram of a method for monitoring an ultra-long distance reservoir dam body based on a flexible inclinometer according to an embodiment of the present invention, such as Figure 3 As shown, the method includes:

[0103] Step S301: obtaining 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;

[0104] Step S302: Based on the survey data, a posterior sample of the random field model parameters is obtained through Bayesian inversion analysis, and reliability analysis is performed based on the posterior sample 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;

[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+ith moment.

[0110] Furthermore, the survey data is preprocessed:

[0111] De-noising: Survey data may contain various noises that will interfere with the authenticity and accuracy of the data. Therefore, appropriate denoising methods, such as window moving polynomial smoothing, wavelet denoising, etc., 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 the accuracy and reliability of the data.

[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 preprocessed data in a secure and reliable database for subsequent query and analysis.

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

[0118] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 programs 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 site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access 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 hard disk.

[0119] Those of ordinary skill 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 to be beyond the scope of this application.

[0120] Those skilled in the art can 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 the present 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 only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, 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 separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Ultra-long distance reservoir dam monitoring system based on flexible inclinometer, characterized by: 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; The data acquisition module is used to acquire 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 the posterior samples of the random field model parameters through Bayesian inversion analysis based on the survey data, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the reservoir dam, and obtain the stability reliability of the reservoir dam 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.

2. The ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to claim 1 is characterized in that: Based on the survey data, the posterior samples of the random field model parameters are obtained through Bayesian inversion analysis. 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 indicates 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 is provided; Kj 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, and I[G(θ)≤0] means that when G(θ)≤0, I[G(θ)≤0] is 1, otherwise I[G(θ)≤0] is 0.

3. The ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to claim 2 is characterized in that: The specific process of obtaining the stability reliability of the reservoir dam based on the posterior failure probability is as follows: An inverse proportional function between the posterior failure probability and the stability reliability of the reservoir dam is established: Among them, wd represents the stability reliability of the reservoir dam.

4. The ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to claim 1 is characterized in that: The specific process of obtaining the predicted value of reservoir dam settlement based on survey data includes the following: 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; The maximum allowable settlement correction factor β is obtained based on the soil composition: The weights corresponding to the soil components of the reservoir dam body are determined based on the soil components 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; Substitute the proportion and weight of the soil composition of the reservoir dam into the correlation formula to calculate the maximum allowable settlement correction coefficient β, which is as follows: 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; 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: Where ρ is the settlement coefficient of the reservoir dam, ρ = 2 × 10 -3 , β is the slope of the reservoir dam.

5. The ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to claim 1 is characterized in that: Based on the stability reliability of the reservoir dam and the predicted value of the reservoir dam settlement, the early warning signal is generated. The process includes: 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, BPS represents the predicted settlement value of the reservoir dam, 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 a warning signal; if not, do not generate a warning signal.

6. The ultra-long distance reservoir dam monitoring system based on 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.

7. The ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to claim 6 is characterized in that: The single section length of the flexible inclinometer used to collect reservoir dam survey data is 25cm / 50cm / 100cm.

8. The ultra-long distance reservoir dam monitoring method based on flexible inclinometer is applicable to the ultra-long distance reservoir dam monitoring system based on flexible inclinometer according to any one of claims 1 to 7, characterized in that: Methods include: The survey data of the reservoir dam at different collection points are obtained based on the flexible inclinometer, wherein the survey data includes the displacement data of the reservoir dam, the spatial position data of the collection points and the 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, and reliability analysis is performed based on the posterior sample 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; Based on the survey data, the predicted value of reservoir dam settlement is obtained; The stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement are obtained, and an early warning signal is generated based on the stability reliability of the reservoir dam body and the predicted value of the reservoir dam body settlement.

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