Method and system for drawing up deformation monitoring indexes of multiple measuring points of concrete dam

Abstract

The invention discloses a method and system for drawing up a multi-measuring-point deformation monitoring index of a concrete dam, and relates to the technical field of drawing up the deformation monitoring index of the concrete dam, and the method comprises the steps: collecting key measuring points and deformation sequence data of deformation of the concrete dam, and constructing a standardized matrix of a multi-measuring-point deformation monitoring sequence; solving a correlation coefficient matrix of the standardized matrix and eigenvalue components of the correlation coefficient matrix; calculating a cumulative variance contribution rate, and determining a correlation component and a discreteness component of the multi-measurement-point deformation monitoring sequence according to a threshold value; and calculating an annual maximum value and an annual minimum value to obtain an annual extreme value sequence, calculating a probability density function of the annual extreme value sequence by using a maximum entropy method, and determining a monitoring threshold range according to a typical small probability method. According to the method, the monitoring information of a plurality of deformation measuring points can be considered at the same time, the monitoring indexes are respectively drawn up for the correlation component and the discreteness component of the deformation of the multiple measuring points, the two groups of monitoring indexes can be fused for use, the overall deformation form of the concrete dam is reflected, and the index meaning is richer and more comprehensive.

Description

Technical Field

[0001] The present invention relates to the technical field of formulating deformation monitoring indicators of concrete dams, and more particularly to a method and system for formulating deformation monitoring indicators of multiple measuring points of concrete dams. Background Art

[0002] As an important infrastructure in the process of water conservancy construction, concrete dams play a huge comprehensive role in flood control, power generation, navigation, irrigation and other aspects. Considering the locational importance and risk of accidents of water conservancy projects, how to ensure the safe service of concrete dams has become a public safety issue related to social stability and economic development. During the construction of concrete dams, a large number of in-situ observation instruments are deployed, which can provide rich data for the analysis and diagnosis of the safety form of concrete dams. Deformation is the most intuitive reflection of the safety form of concrete dams. Formulating deformation monitoring indicators for concrete dams based on in-situ monitoring data is an important means of perceiving and feedback-controlling the safety form of concrete dams.

[0003] Traditional deformation monitoring index formulation methods mainly include confidence interval method, typical small probability method and limit state method. However, these methods mostly focus on the changing law of single-side point deformation data. As a large-scale structural system, the deformation behavior of concrete dams usually has complex spatial distribution characteristics. The monitoring indicators based on single-side point deformation data are significantly one-sided and inaccurate, making it difficult to achieve effective perception of the deformation behavior of concrete dams.

[0004] Therefore, how to propose a method and system for formulating deformation monitoring indicators of multiple measuring points of concrete dams, consider the correlation and discreteness of multi-measuring point monitoring data, and comprehensively use principal component analysis and maximum entropy theory to comprehensively perceive and feedback-control the deformation morphology of concrete dams is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for formulating deformation monitoring indicators of multiple measuring points of a concrete dam. Considering the correlation and discreteness of the monitoring data of multiple measuring points, the principal component analysis method and the maximum entropy theory are comprehensively used to comprehensively perceive and feedback-control the deformation form of the concrete dam. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam, comprising:

[0007] Collect key measurement points and deformation sequence data of concrete dam deformation, and build a standardized matrix of multi-measurement point deformation monitoring sequence;

[0008] Solve the correlation coefficient matrix and its eigenvalue components of the standardized matrix;

[0009] Calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold;

[0010] The annual maximum and annual minimum values ​​of the correlation component and the discrete component are calculated respectively to obtain the annual extreme value sequence, the probability density function of the annual extreme value sequence is calculated using the maximum entropy method, and the monitoring threshold range of the correlation component and the discrete component is determined based on the probability density function and the typical small probability method.

[0011] Optionally, the step of constructing a standardized matrix of a multi-measurement point deformation monitoring sequence includes:

[0012] Constructing a multi-point deformation monitoring sequence for concrete dams ij (0≤i≤n,0≤j≤m), and normalize:

[0013] Where: z ij is the standardized deformation monitoring sequence, and the average value of the deformation monitoring sequence is The standard deviation is

[0014] Optionally, the correlation coefficient matrix of the standardized matrix is ​​solved including:

[0015] Among them, Z is the normalization matrix, Z T is the transposed matrix of the standardized matrix, and R is the correlation coefficient matrix R of the standardized matrix Z.

[0016] Optionally, solving the eigenvalue component includes:

[0017] Determine the eigenvalue component according to the characteristic equation of the correlation coefficient matrix R;

[0018] The characteristic equation of the correlation coefficient matrix R is |R-βI p |=0, β is the assumed eigenvalue of matrix R, I p is the n-order identity matrix;

[0019] Calculate the m eigenvalues ​​of the correlation coefficient matrix R: β1, β2, …, β m (β1≥β2≥…≥β m ≥0);

[0020] β i The corresponding unit eigenvector is the i-th order component u i .

[0021] Optionally, the calculating the cumulative variance contribution rates of different components includes:

[0022] Solve the cumulative variance contribution rate of different components;

[0023] The calculation formula for the cumulative variance contribution rate is:

[0024] The threshold c is set according to the deformation component characteristics. The value of c is usually 0.85-0.95. The correlation component U of the multi-point deformation monitoring sequence is determined based on whether the cumulative variance contribution rate of the current k components is greater than the threshold c. p and discrete component U e .

[0025] Optionally, the correlation component U p =u pi (0≤i≤n) and the discrete component U e =u ei (0≤i≤n) is determined by the following formula:

[0026] Optionally, the step of respectively calculating the annual maximum and annual minimum values ​​of the correlation component and the discrete component to obtain the annual extreme value sequence comprises: respectively calculating the correlation component U p and discrete component U e The annual maximum and annual minimum values ​​form its annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l), where l is the number of years included in the sequence.

[0027] Optionally, the method of calculating the probability density function of the annual extreme value sequence using the maximum entropy method includes:

[0028] Construct the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l) probability density function f(x) at which the information entropy H(x) reaches its maximum value, and the probability density function f(x) satisfies the maximization objective function and corresponding constraints;

[0029] The Lagrange multiplier method is used to establish the Lagrange function to solve the above problem;

[0030] The initial value of the Lagrange multiplier is determined, the Lagrange function is functionally coupled with the probability density function, and the coupling function is solved by a nonlinear programming method to obtain the determined value of the Lagrange multiplier.

[0031] Optionally, the monitoring threshold ranges of the correlation component and the discrete component are determined according to the probability density function and the typical small probability method, including:

[0032] The correlation component U is determined by the formula pThe monitoring threshold range and discrete component U e The monitoring threshold range

[0033]

[0034] Among them, f max (δ) and f min (δ) is the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini The probability density function of (0≤i≤l).

[0035] Optionally, a system for formulating deformation monitoring indicators of multiple measuring points of a concrete dam includes:

[0036] Acquisition module: used to collect key measurement points and deformation sequence data of concrete dam deformation;

[0037] Construction module: used to construct a standardized matrix for multi-point deformation monitoring sequence;

[0038] Solving module: used to solve the correlation coefficient matrix and its eigenvalue components of the standardized matrix;

[0039] Threshold determination module: used to calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold;

[0040] Calculation module: used to calculate the annual maximum and annual minimum values ​​of the correlation component and the discrete component respectively to obtain the annual extreme value sequence, and use the maximum entropy method to calculate the probability density function of the annual extreme value sequence;

[0041] Monitoring threshold range determination module: used to determine the monitoring threshold range of correlation components and discrete components based on probability density function and typical small probability method.

[0042] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for formulating deformation monitoring indicators of multiple measuring points of a concrete dam, which has the following beneficial effects:

[0043] The present invention proposes a method for formulating a multi-measurement point deformation monitoring index of a concrete dam, including: collecting key measurement points and deformation sequence data of the concrete dam deformation, constructing a standardized matrix of the multi-measurement point deformation monitoring sequence; solving the correlation coefficient matrix and its eigenvalue components of the standardized matrix; calculating the cumulative variance contribution rate of different components, and determining the correlation component and discrete component of the multi-measurement point deformation monitoring sequence according to the threshold; respectively calculating the annual maximum value and annual minimum value of the correlation component and the discrete component to obtain the annual extreme value sequence, using the maximum entropy method to calculate the probability density function of the annual extreme value sequence, and determining the monitoring threshold range of the correlation component and the discrete component according to the probability density function and the typical small probability method. The present invention can simultaneously consider the monitoring information of multiple deformation measurement points, and can reflect the overall deformation form of the concrete dam compared to the traditional single measurement point deformation index, and the index meaning is richer and more comprehensive; respectively formulate monitoring indicators for the correlation component and the discrete component of the multi-measurement point deformation, and can fuse the two groups of monitoring indicators. If the correlation component monitoring index is abnormal, it can be judged that the overall deformation of the concrete dam may have an unfavorable trend; if the random component is abnormal, it can be judged that the local deformation measurement point of the concrete dam may be inaccurately monitored. The deformation monitoring index proposed by the present invention is an interval-type index, which is more reasonable and effective than the traditional unilateral index. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0045] Figure 1 A schematic flow chart of a method for formulating deformation monitoring indicators of multiple measuring points in a concrete dam provided by the present invention.

[0046] Figure 2 A structural framework diagram of a concrete dam multi-measurement point deformation monitoring index formulation system provided by the present invention.

[0047] Figure 3 This is a distribution diagram of the cumulative variance contribution rate of different components provided by the present invention.

[0048] Figure 4 A schematic diagram of the correlation component and discrete component of the multi-measurement point deformation monitoring sequence provided by the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] The embodiment of the present invention discloses a method for formulating a multi-measurement point deformation monitoring index for a concrete dam. Figure 1 As shown, including:

[0051] Collect key measurement points and deformation sequence data of concrete dam deformation, and build a standardized matrix of multi-measurement point deformation monitoring sequence;

[0052] Solve for the correlation coefficient matrix and its eigenvalue components of the standardized matrix;

[0053] Calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold;

[0054] The annual maximum and annual minimum values ​​of the correlation component and the discrete component are calculated respectively to obtain the annual extreme value sequence, the probability density function of the annual extreme value sequence is calculated using the maximum entropy method, and the monitoring threshold range of the correlation component and the discrete component is determined based on the probability density function and the typical small probability method.

[0055] Furthermore, the construction of a standardized matrix of a multi-measurement point deformation monitoring sequence includes:

[0056] According to the shape characteristics and monitoring scheme of the concrete dam, the key measuring points of the concrete dam deformation are selected, and the deformation sequences of m key measuring points and n moments are sorted and arranged in chronological order. ij (0≤i≤n,0≤j≤m) for standardization.

[0057] Furthermore, the "standardization" process can be performed by the following formula:

[0058] Among them, z ij is the standardized deformation monitoring sequence, the average value of the deformation monitoring sequence Standard Deviation Based on this, the standardized matrix of the deformation monitoring sequence of multiple measuring points can be obtained

[0059] Furthermore, the correlation coefficient matrix of the standardized matrix is ​​solved as follows:

[0060] Among them, Z is the normalization matrix, Z Tis the transposed matrix of the standardized matrix, and R is the correlation coefficient matrix R of the standardized matrix Z.

[0061] Further, solving the eigenvalue component includes:

[0062] Determine the eigenvalue component according to the characteristic equation of the correlation coefficient matrix R;

[0063] The characteristic equation of the correlation coefficient matrix R is |R-βI p |=0, β is the assumed eigenvalue of matrix R, I p is the n-order identity matrix;

[0064] Calculate the m eigenvalues ​​of the correlation coefficient matrix R: β1, β2, …, β m (β1≥β2≥…≥β m ≥0);

[0065] β i The corresponding unit eigenvector is the i-th order component u i .

[0066] Further, the calculation of the cumulative variance contribution rate of different components includes:

[0067] Solve the cumulative variance contribution rate of different components;

[0068] The calculation formula for the cumulative variance contribution rate is:

[0069] The threshold c is set according to the deformation component characteristics. The value of c is usually 0.85-0.95. The correlation component U of the multi-point deformation monitoring sequence is determined based on whether the cumulative variance contribution rate of the current k components is greater than the threshold c. p and discrete component U e .

[0070] Furthermore, the correlation component U p =u pi (0≤i≤n) and the discrete component U e =u ei (0≤i≤n) is determined by the following formula:

[0071] Further, the step of respectively calculating the annual maximum and annual minimum values ​​of the correlation component and the discrete component to obtain the annual extreme value sequence comprises: respectively calculating the correlation component U p and discrete component U e The annual maximum and annual minimum values ​​form its annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini(0≤i≤l), where l is the number of years included in the sequence.

[0072] Furthermore, the method of calculating the probability density function of the annual extreme value sequence using the maximum entropy method includes:

[0073] Construct the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l) probability density function f(x) at which the information entropy H(x) reaches its maximum value, and the probability density function f(x) satisfies the maximization objective function and corresponding constraints;

[0074] The Lagrange multiplier method is used to establish the Lagrange function to solve the above problem;

[0075] The initial value of the Lagrange multiplier is determined, the Lagrange function is functionally coupled with the probability density function, and the coupling function is solved by a nonlinear programming method to obtain the determined value of the Lagrange multiplier.

[0076] In a specific implementation, the use of the maximum entropy method to calculate the probability density function of the annual extreme value sequence specifically includes:

[0077] S1: Construct the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l), the probability density function f(x) at which the information entropy H(x) reaches its maximum value, and its objective function is as follows:

[0078] maximize:

[0079] obey:

[0080]

[0081] Where: R is the integration domain, is the i-th order sample moment of the annual extreme value series.

[0082] S2: Use the Lagrange multiplier method to establish the following Lagrange function:

[0083]

[0084] According to the stationary point theory, the above formula is transformed into:

[0085]

[0086] The analytical form of the above formula, that is, the analytical form of the maximum entropy probability density function f(x), can be expressed as:

[0087]

[0088] S3: Determine the Lagrange multipliers (λ0,λ1,…,λ N ), and substitute equation (6) into equation (2) to obtain:

[0089]

[0090] Then, substituting equations (6) and (9) into equation (3), we obtain:

[0091]

[0092] S4: To facilitate numerical solution, formula (10) is rewritten as:

[0093]

[0094] Among them, r i is the residual, which can be approached to 0 by numerical methods. The nonlinear programming solution can be used When r<ε or |r i |<ε, the formula is considered to converge, and the Lagrange multiplier (λ0,λ1,…,λ N ).

[0095] Furthermore, the monitoring threshold ranges of the correlation component and the discrete component are determined according to the probability density function and the typical small probability method, including:

[0096] The correlation component U is determined by the formula p The monitoring threshold range and discrete component U e The monitoring threshold range

[0097]

[0098] Among them, f max (δ) and f min (δ) is the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini The probability density function of (0≤i≤l).

[0099] Specifically, the maximum entropy method is used to calculate the correlation component U p and discrete component U e The steps of the probability density function of the annual extreme value series are the same, and the correlation component U p and discrete component U e The monitoring threshold range is determined in the same way.

[0100] In a specific implementation, a system for formulating deformation monitoring indicators of multiple measuring points of a concrete dam is provided. Figure 2 As shown, including:

[0101] Acquisition module: used to collect key measurement points and deformation sequence data of concrete dam deformation;

[0102] Construction module: used to construct a standardized matrix for multi-point deformation monitoring sequence;

[0103] Solving module: used to solve the correlation coefficient matrix and its eigenvalue components of the standardized matrix;

[0104] Threshold determination module: used to calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold;

[0105] Calculation module: used to calculate the annual maximum and annual minimum values ​​of the correlation component and the discrete component respectively to obtain the annual extreme value sequence, and use the maximum entropy method to calculate the probability density function of the annual extreme value sequence;

[0106] Monitoring threshold range determination module: used to determine the monitoring threshold range of correlation components and discrete components based on probability density function and typical small probability method.

[0107] In a specific embodiment, a concrete gravity arch dam of a multi-year regulating reservoir is taken as an example. The gravity arch dam consists of 28 dam sections, with a dam top elevation of 126.3 m, a maximum dam height of 76.3 m, a normal high water level of 119.0 m, and a dead water level of 101.0 m. In order to monitor the overall deformation of the gravity arch dam, it is necessary to formulate its deformation monitoring index based on the information of multiple measuring points:

[0108] S11: According to the shape characteristics and monitoring plan of the concrete gravity arch dam, the DP8-UP, DP8-DOWN, DP18-UP, DP18-DOWN, DP26-UP, and DP26-DOWN measuring points of the 8#, 18#, and 26# dam sections were selected as key deformation measuring points, and the deformation sequences of these 6 key measuring points with dates from 1972.07.18 to 2013.07.11 were sorted and arranged in chronological order, and the sequence was standardized to obtain its standardized matrix.

[0109] S2: Solve the correlation coefficient matrix and its eigenvalues ​​of the standardized matrix.

[0110] S3: Solve the cumulative variance contribution rate of different components, such as Figure 3 As shown in the figure, the threshold is set to 90% in advance, and the cumulative variance contribution rate of the first two components is 91.37%, which is greater than the threshold of 90%, thereby determining the correlation component U of the multi-point deformation monitoring sequence. pand discrete component U e ,like Figure 4 shown.

[0111] S4: Calculate the correlation components U separately p and discrete component U e The annual maximum and annual minimum values ​​form its annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l), and the probability density function of the annual extreme value sequence is calculated using the maximum entropy method:

[0112] f max (U p )=exp(-2.6149+0.2725x-0.0947x 2 +0.0245x 3 -0.0023x 4 );

[0113] f min (U p )=exp(-53.6424-22.7234x-3.7109x 2 -0.2675x 3 -0.0072x 4 );

[0114] f max (U e )=exp(-74.4431-37.6518x-6.9236x 2 -0.5426x 3 -0.0157x 4 );

[0115] f min (U e )=exp(-29.4397-28.0643x-9.4202x 2 -1.2896x 3 -0.0648x 4 );

[0116] S5: Using the typical small probability method, determine the correlation component U at the significance level of 5% and 1% respectively. p and discrete component U e The monitoring thresholds are shown in Table 1.

[0117] Table 1

[0118]

[0119]

[0120] As can be seen from Table 1, (1) the present invention can consider the monitoring information of multiple deformation measuring points at the same time. Compared with the traditional single-measuring point deformation index, it can reflect the overall deformation form of the concrete dam, and the index meaning is richer and more comprehensive. (2) The present invention proposes monitoring indicators for the correlation component and discrete component of the deformation of multiple measuring points respectively, and the two sets of monitoring indicators can be integrated and used. If the correlation component monitoring indicator is abnormal, it can be judged that the overall deformation of the concrete dam may have an unfavorable trend; if the random component is abnormal, it can be judged that the local deformation measuring point of the concrete dam may be inaccurately monitored. (3) The deformation monitoring indicator proposed by the present invention is an interval indicator, which is more reasonable and effective than the traditional unilateral indicator.

[0121] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0122] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for formulating deformation monitoring indicators for multiple measuring points of a concrete dam, characterized in that: include: Collect key measurement points and deformation sequence data of concrete dam deformation, and build a standardized matrix of multi-measurement point deformation monitoring sequence; Solve for the correlation coefficient matrix and its eigenvalue components of the standardized matrix; Calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold; The annual maximum and annual minimum values ​​of the correlation component and the discrete component are calculated respectively to obtain the annual extreme value sequence, the probability density function of the annual extreme value sequence is calculated using the maximum entropy method, and the monitoring threshold range of the correlation component and the discrete component is determined based on the probability density function and the typical small probability method.

2. A method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1, characterized in that: The standardized matrix for constructing a multi-measurement point deformation monitoring sequence includes: Constructing a multi-point deformation monitoring sequence for concrete dams ij (0≤i≤n,0≤j≤m), and normalize: Where: z ij is the standardized deformation monitoring sequence, and the average value of the deformation monitoring sequence is The standard deviation is 3. The method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1 is characterized in that: The correlation coefficient matrix for solving the standardized matrix includes: Among them, Z is the normalization matrix, Z T is the transposed matrix of the standardized matrix, and R is the correlation coefficient matrix R of the standardized matrix Z.

4. The method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1 is characterized in that: Solving the eigenvalue components includes: Determine the eigenvalue component according to the characteristic equation of the correlation coefficient matrix R; The characteristic equation of the correlation coefficient matrix R is |R-βI p |=0, β is the assumed eigenvalue of matrix R, I p is the n-order identity matrix; Calculate the m eigenvalues ​​of the correlation coefficient matrix R: β1, β2, …, β m (β1≥β2≥…≥β m ≥0); β i The corresponding unit eigenvector is the i-th order component u i .

5. The method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1 is characterized in that: The calculation of the cumulative variance contribution rate of different components includes: Solve the cumulative variance contribution rate of different components; The calculation formula for the cumulative variance contribution rate is: According to the deformation component characteristics, a threshold c is set, and the correlation component U of the multi-measurement point deformation monitoring sequence is determined based on whether the cumulative variance contribution rate of the current k components is greater than the threshold c. p and discrete component U e .

6. A method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 5, characterized in that: The correlation component U p =u pi (0≤i≤n) and the discrete component U e =u ei (0≤i≤n) is determined by the following formula:

7. The method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1, characterized in that: The method of respectively calculating the annual maximum and annual minimum values ​​of the correlation component and the discrete component to obtain the annual extreme value sequence comprises: respectively calculating the correlation component U p and discrete component U e The annual maximum and annual minimum values ​​form its annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l), where l is the number of years included in the sequence.

8. The method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1 is characterized in that: The method of calculating the probability density function of the annual extreme value sequence using the maximum entropy method includes: Construct the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini (0≤i≤l) probability density function f(x) at which the information entropy H(x) reaches its maximum value, and the probability density function f(x) satisfies the maximization objective function and corresponding constraints; The Lagrange multiplier method is used to establish the Lagrange function to solve the above problem; The initial value of the Lagrange multiplier is determined, the Lagrange function is functionally coupled with the probability density function, and the coupling function is solved by a nonlinear programming method to obtain the determined value of the Lagrange multiplier.

9. A method for formulating deformation monitoring indicators of multiple measuring points of a concrete dam according to claim 1, characterized in that: The monitoring threshold ranges of the correlation component and the discrete component determined according to the probability density function and the typical small probability method include: The correlation component U is determined by the formula p The monitoring threshold range and discrete component U e The monitoring threshold range Among them, f max (δ) and f min (δ) is the annual extreme value sequence X max =x maxi (0≤i≤l) and X min =x mini The probability density function of (0≤i≤l).

10. A system for formulating deformation monitoring indicators for multiple measuring points of concrete dams, characterized in that: include: Acquisition module: used to collect key measurement points and deformation sequence data of concrete dam deformation; Construction module: used to construct a standardized matrix for multi-point deformation monitoring sequence; Solving module: used to solve the correlation coefficient matrix and its eigenvalue components of the standardized matrix; Threshold determination module: used to calculate the cumulative variance contribution rate of different components, and determine the correlation component and discrete component of the multi-point deformation monitoring sequence according to the threshold; Calculation module: used to calculate the annual maximum and annual minimum values ​​of the correlation component and the discrete component respectively to obtain the annual extreme value sequence, and use the maximum entropy method to calculate the probability density function of the annual extreme value sequence; Monitoring threshold range determination module: used to determine the monitoring threshold range of correlation components and discrete components based on probability density function and typical small probability method.

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