Dam appearance global deformation monitoring and early warning method

Abstract

The present invention discloses a method for monitoring and early warning of global deformation of dam appearance, namely: firstly obtain the three-dimensional point cloud coordinate set of the entire dam surface outside the dam at different times, perform multivariate regression statistical analysis on the three-dimensional point cloud coordinate set of the dam surface corrected at time t, and calculate and determine the multivariate continuous function set of the entire dam surface at time t; perform multivariate regression statistical analysis on the three-dimensional point cloud coordinate data set of the entire dam surface corrected at time t+1, and calculate and determine the multivariate continuous function set of the entire dam surface at time t+1; compare the multivariate continuous function set of the entire dam surface at time t and time t+1, calculate the difference in coordinate values ​​at the two times, so as to quickly obtain the continuous deformation of the entire dam appearance, and divide the early warning level according to the change. The present invention can quickly identify and locate the micro deformation and area of ​​the entire dam appearance, issue an early warning, and facilitate the adoption of reasonable engineering and technical measures to effectively control the further expansion of the dam appearance deformation and ensure the safety of the dam.

Description

Technical Field

[0001] The present invention relates to a dam appearance deformation monitoring and early warning method, and in particular to a method for monitoring the overall deformation of the dam body appearance of a concrete dam or an earth-rock dam. The present invention belongs to the technical field of dam operation safety monitoring. Background Art

[0002] Concrete dams, earth-rock dams and other water-retaining structures are subject to external loads such as water pressure and temperature during operation. Considering the evolution of the characteristic parameters of dam construction materials during long-term operation, the dam structure will produce different degrees of deformation. When the deformation exceeds the design, calculation and analysis indicators or the requirements of current specifications, there is a safety hazard. Certain engineering measures need to be taken to control the development of deformation and ensure the safe operation of the dam.

[0003] At present, the most widely used methods for monitoring the internal and external deformation of dams in engineering practice include the tension line method, the sight line method, the positive (inverted) plumb line method, the inclinometer observation method, the vacuum laser observation method, etc. In recent years, with the improvement of automation level, a number of technologies with high measurement accuracy, easy installation and automated observation, such as measuring robots and flexible inclinometers, have been rapidly developed and applied. The advantages of the above deformation observation methods are high accuracy and easy automatic data collection. The disadvantages are that only the local part of the dam body, that is, the dam deformation reflected by the points or lines on a certain monitoring section, can be obtained, and the entire dam, that is, the continuous deformation of the dam surface and body, cannot be observed. As a water-retaining structure, the earth-rock dam or concrete dam usually has a spatial span of hundreds to thousands of meters. The existing observation methods cannot cover the blind areas between the monitoring sections. Existing engineering hidden dangers, dangerous conditions and accident cases show that the probability of abnormal deformation in the monitoring blind area is extremely high. If the abnormal deformation is not discovered in time and engineering control and repair measures are not taken, serious accidents will occur. In summary, realizing deformation safety monitoring of the entire dam is of great significance to ensure the long-term operation safety of the dam. Summary of the invention

[0004] In view of the above reasons, an object of the present invention is to provide a method for monitoring and warning of the global deformation of the dam body appearance.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for monitoring and early warning of global deformation of dam appearance, which comprises the following steps:

[0006] S1. Obtain the three-dimensional point cloud coordinate set of the entire appearance of the dam body;

[0007] S1.1. Use drone visual photography and laser radar equipment to obtain the dam surface image and laser coordinate data of the entire dam body. Use the method of matching adjacent image feature points and laser radar verification to process the captured global dam surface image and obtain the 3D point cloud coordinate dataset A of the dam surface at time t.t ={x ti ,y ti ,z ti}, where i = 1, 2, 3, ..., p, and p is the number of three-dimensional point cloud data representing the overall appearance features of the dam body;

[0008] S1.2. According to the spatial position of the dam surface, the dam surface is evenly divided into m dam surface fitting areas. The three-dimensional point cloud coordinate dataset B of the entire dam surface is t Divided into {B t1 ,B t2 ,B t3 ,…,B tm}, where B tm It represents the three-dimensional point cloud coordinate data set of the dam surface fitting area numbered m at time t;

[0009] S2, the three-dimensional point cloud coordinate set B of the entire dam surface at time t t Perform multivariate regression statistical analysis to determine the multivariate continuous function set C of the entire dam surface t ={f t1 (x,y,z),f t2 (x,y,z),f t3 (x,y,z),…,f tm (x,y,z)}, multivariate continuous function set C t The calculation steps are as follows:

[0010] S2.1, based on the 3D point cloud coordinate dataset B of the entire dam surface tm , solve the multivariate continuous function f of each dam surface fitting area separately tm (x, y, z) and the coefficient of determination R of each dam surface fitting area 2 :

[0011] 1) Assume that the dam surface multivariate continuous function f tm The (x,y,z) model is: Among them, a jk is the function coefficient, j is the number of terms in x, and k is the number of terms in y;

[0012] 2) Use the residual sum of squares S(x,y,z) as the evaluation function and substitute it into the coordinate set B tm , and set the restriction function at the same time: Solution The optimal coefficient solution set {a 00 ,a 01 ,a 02 ,a 03 ,a 10 ,...,a jk}, we get the multivariate continuous function f of each dam surface fitting area at the current time t tm (x,y,z);

[0013] 3) Calculate the multivariate continuous function f of each dam surface fitting area tm Coefficient of determination of (x,y,z) Among them, SSR is the regression sum of squares, and SST is the total sum of squares;

[0014] S2.2. Determine the coefficient R of each dam surface fitting area 2 Is it greater than 0.9?

[0015] If the coefficient of determination R 2 >0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) meets the accuracy requirement, and execute step S2.4;

[0016] If the coefficient of determination R 2 ≤0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) does not meet the accuracy requirement, and execute step S2.3;

[0017] S2.3. For continuous functions f that do not meet the accuracy requirements tm (x, y, z) coordinate set B of the dam surface fitting area tm , based on the geometric construction, the dam surface fitting area is divided into four equal parts, then B tm It consists of four sub-areas {B t(m1) ,B t(m2) ,B t(m3) ,B t(m4)}, perform step S2.1 fitting and solving on the re-divided coordinate set area. If the determination coefficient R of the re-fitted multivariate continuous function is 2 If it is still not greater than 0.9, the coordinate set of the dam surface fitting area that still does not meet the accuracy requirement after division is further divided into four equal parts until the determination coefficient R of the multivariate continuous function of all dam surface fitting areas within the dam surface is 2 If both are greater than 0.9, the division is stopped;

[0018] At this time, the mth dam surface fitting area B tm There are b (b ≥ 1) sub-regions {B t(m1) ,B t(m2) ,B t(m3) ,…,B t(mb)}, each sub-region has a high-precision multivariate continuous function f tmb (x,y,z);

[0019] S2.4 When all sub-regions in m regions have a determination coefficient R2 When all are greater than 0.9, there are a total of n multivariate continuous functions on the dam surface at time t;

[0020] S3, the three-dimensional point cloud coordinate data set B of the entire dam surface at time t+1 (t+1)m Repeat the above steps S1 and S2 to determine the multivariate continuous function set C of the entire dam surface at time t+1 t+1 ={f (t+1)1 (x,y,z),f (t+1)2 (x,y,z),f (t+1)3 (x,y,z),…,f (t+1)m (x, y, z)}, there are a total of l multivariate continuous functions on the dam surface at time t+1;

[0021] S4, compare the multivariate continuous function set C of the entire dam surface at time t t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 , determine whether the dam surface is deformed, and divide the warning level according to the changes. The specific methods are as follows:

[0022] (1) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 The number of functions is equal, that is, n = l, and the function model in the same area has not changed, indicating that the dam surface has not deformed and no early warning is needed;

[0023] (2) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 The number of functions is equal, that is, n = l, but the function model in the same area changes, and a first-level warning is issued for the changed area based on the dam surface area number;

[0024] (3) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and (ln) / 4≤2, it means that the dam surface is partially deformed, and a second-level warning is issued for the changed area according to the dam surface area number;

[0025] (4) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and 2<(ln) / 4≤5, it means that deformation has occurred in many places on the dam surface, and a three-level warning is issued for the changed area according to the dam surface area number;

[0026] (5) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and 5<(ln) / 4, it means that the dam surface is seriously deformed, and a fourth-level warning is issued for the changed area based on the dam surface area number. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the method for monitoring and early warning of global deformation of dam appearance according to the present invention. DETAILED DESCRIPTION

[0028] The structure and features of the present invention are described in detail below in conjunction with the accompanying drawings and embodiments. It should be noted that various modifications can be made to the embodiments disclosed herein, and therefore, the embodiments disclosed in the specification should not be regarded as limiting the present invention, but are merely examples of embodiments, the purpose of which is to make the features of the present invention obvious.

[0029] like Figure 1 As shown, the present invention realizes the global deformation monitoring and early warning of the dam body appearance through the following method, and the specific method is as follows:

[0030] S1. Obtain the three-dimensional point cloud coordinate set of the entire appearance of the dam body.

[0031] S1.1. Use drone visual photography and laser radar equipment to obtain the dam surface image and laser coordinate data of the entire dam body. Use the method of matching adjacent image feature points and laser radar verification to process the captured global dam surface image and obtain the 3D point cloud coordinate dataset A of the dam surface at time t. t ={x i ,y i ,z i}, where i = 1, 2, 3, ..., p, and p is the number of three-dimensional point cloud data representing the overall appearance features of the dam body;

[0032] In order to obtain a more accurate three-dimensional point cloud coordinate set of the entire dam surface, the present invention uses the data of a local area and / or a certain dam section obtained by traditional measurement methods such as the plumb line method or the sight line method or the total station to correct the three-dimensional point cloud coordinate data set of the entire appearance of the dam body obtained in step S1.1, forming a more accurate three-dimensional point cloud coordinate data set of the entire dam surface Bt = {x i ,y i ,z i}, where i=1,2,3,…,p.

[0033] For example, when obtaining the 3D point cloud coordinate dataset A of the dam body appearance tWith A t-1 Then, the three-dimensional point cloud coordinate data sets A at two different times are t-1 With A t In the dam, the coordinate positions corresponding to the existing traditional external deformation observation points are marked and recorded as A i0t-1 ={x i0t-1 ,y i0t-1 ,z i0t-1} and A i0t ={x i0t ,y i0t ,z i0t}, where A 0t-1 is the three-dimensional point cloud coordinates of the traditional external deformation observation point of the dam at time t-1, A 0t is the three-dimensional point cloud coordinates of the traditional external deformation observation point of the dam at time t, ΔA i0 ={x i0t -x i0t-1 ,y i0t -y i0t-1 ,z i0t -z i0t-1} is the calculated spatial three-dimensional coordinate change value of the marked point from time t-1 to time t, that is, the deformation value of the dam's external deformation observation point calculated from time t-1 to time t by using the three-dimensional point cloud coordinates. At this time, the deformation data of the dam's external deformation observation point from time t-1 to time t measured by traditional observation technology is ΔB it ={Δx it ,Δy it ,Δz it} (When there is no measurement error, the two are theoretically completely equal). In engineering practice, the deformation values ​​obtained by traditional deformation observation technology are generally used as the standard, and the traditional observation deformation data set ΔB is used. t The measured results of the three-dimensional point cloud coordinate dataset A t The correction method is that when the external deformation monitoring point of the dam coincides with the three-dimensional point cloud coordinates, the three-dimensional point cloud coordinates at time t-1 and ΔB it Add together to get the corrected 3D point cloud coordinates B at time t it , other A t The three-dimensional coordinates in are corrected by internal interpolation. In summary, the corrected three-dimensional point cloud coordinate dataset B of the entire dam surface is obtained. t ={x ti ,y ti ,z ti}, where i=1,2,3,…,p.

[0034] S1.2. In order to quickly locate the position of the dam surface deformation area, the present invention evenly divides the dam surface into m dam surface fitting areas according to the spatial position of the dam surface. The corrected three-dimensional point cloud coordinate data set B of the entire dam surface is t Divided into {B t1 ,B t2 ,B t3 ,…,B tm}, where B tm Represents the three-dimensional point cloud coordinate data set of the dam surface fitting area numbered m at time t.

[0035] In a preferred embodiment of the present invention, the area of ​​each dam surface fitting area is not more than 1m 2 .

[0036] S2, the three-dimensional point cloud coordinate set B of the entire dam surface at time t t Perform multivariate regression statistical analysis to determine the multivariate continuous function set C of the entire dam surface t ={f t1 (x,y,z),f t2 (x,y,z),f t3 (x,y,z),…,f tm (x,y,z)}, multivariate continuous function set C t The calculation steps are as follows:

[0037] S2.1 Based on the 3D point cloud coordinate dataset B of the entire dam surface tm , solve the multivariate continuous function f of each dam surface fitting area separately tm (x, y, z) and the coefficient of determination R of each dam surface fitting area 2 :

[0038] 1) The present invention assumes that the dam surface multivariate continuous function f tm The (x,y,z) model is: Among them, a jk is the function coefficient, j is the number of terms in x, and k is the number of terms in y;

[0039] 2) Use the residual sum of squares S(x,y,z) as the evaluation function and substitute it into the coordinate set B tm , and set the restriction function at the same time: Solution The optimal coefficient solution set {a 00 ,a 01 ,a 02 ,a 03 ,a 10 ,...,a jk}, we get the multivariate continuous function f of each dam surface fitting area at the current time t tm (x,y,z);

[0040] 3) Calculate the multivariate continuous function f of each dam surface fitting area tm Coefficient of determination of (x,y,z) Among them, SSR is the regression sum of squares, and SST is the total sum of squares;

[0041] S2.2. Determine the coefficient R of each dam surface fitting area 2 Is it greater than 0.9?

[0042] If the coefficient of determination R 2 >0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) meets the accuracy requirement, and execute step S2.4;

[0043] If the coefficient of determination R 2 ≤0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) does not meet the accuracy requirement, and execute step S2.3;

[0044] S2.3. For continuous functions f that do not meet the accuracy requirements tm (x, y, z) coordinate set B of the dam surface fitting area tm , based on the geometric construction, the dam surface fitting area is divided into four equal parts, then B tm It consists of four sub-areas {B t(m1) ,B t(m2) ,B t(m3) ,B t(m4)}, perform the fitting and solving process of step S2.1 on the re-divided coordinate set area. If the determination coefficient R of the re-fitted multivariate continuous function is 2 If it is still not greater than 0.9, the coordinate set that still does not meet the requirements after division is further divided into four equal parts until the coefficient of determination R of the multivariate continuous function of all areas within the dam surface is 2 If both are greater than 0.9, the division is stopped;

[0045] At this time, the mth dam surface fitting area B tm There are b (b ≥ 1) sub-regions {B t(m1) ,B t(m2) ,B t(m3) ,…,B t(mb)}, each region has a high-precision multivariate continuous function f tmb (x,y,z);

[0046] S2.4 When all sub-regions in m regions have a determination coefficient R 2 When all are greater than 0.9, there are n multivariate continuous functions on the dam surface at time t.

[0047] S3, the three-dimensional point cloud coordinate data set B of the entire dam surface at time t+1 (t+1)m Repeat the above steps S1 and S2 to determine the multivariate continuous function set C of the entire dam surface at time t+1 t+1 ={f (t+1)1 (x,y,z),f (t+1)2 (x,y,z),f (t+1)3 (x,y,z),…,f (t+1)m (x, y, z)}, there are a total of l multivariate continuous functions on the dam surface at time t+1.

[0048] S4. Compare the multivariate continuous function set C of the entire dam surface at time t t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 , quickly identify the deformation of the dam and divide the warning level according to the changes. The specific situation is as follows:

[0049] (1) When the function set C t and C t+1 The number of functions has not changed, that is, n = l, and the function model in the same area has not changed, indicating that the dam surface has not deformed and no early warning is needed;

[0050] (2) When the function set C t and C t+1 The number of functions has not changed, that is, n = l, but the function model in the same area has changed, indicating that the dam surface has undergone a slight deformation. According to the dam surface area number, a first-level warning is issued for the changed area.

[0051] (3) When the function set C t and C t+1 When the number of functions of changes, n≠l, and (ln) / 4≤2, it means that deformation phenomena such as piping and leakage occur locally on the surface of the earth-rock dam; deformation phenomena such as cracking, expansion, and spalling occur locally on the surface of the concrete dam. A secondary warning is issued for the changed area based on the dam surface area number;

[0052] (4) When the function set C t and C t+1 When the number of functions of changes, n≠l, and 2<(ln) / 4≤5, it means that deformation phenomena such as piping and leakage occur in many places on the earth-rock dam surface; deformation phenomena such as cracking, expansion, and spalling occur in many places on the concrete dam surface. According to the dam surface area number, a three-level warning is issued for the changed area;

[0053] (5) When the function set C t and C t+1When the number of functions changes, n≠l, and 5<(ln) / 4, it means that the dam surface is seriously deformed, and a fourth-level warning is issued for the changed area based on the dam surface area number.

[0054] When the dam surface is monitored to be in a level one warning condition, management personnel need to increase the frequency of inspections; when the dam surface is monitored to be in a level two warning condition or above, it is necessary to lower the operating water level and take measures such as strengthening and repairing the dam.

[0055] The present invention is based on the regional division and regression deformation continuous function comparison method, which can quickly realize the identification of deformation area and deformation state. 2 It can quickly identify and locate the deformed area and issue an early warning when a small deformation occurs locally on the dam surface, prompting the adoption of reasonable engineering and technical measures to effectively control the further expansion of the deformation of the dam's appearance and ensure the safety of the dam.

[0056] Finally, it should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and early warning of global deformation of dam appearance, characterized by: It includes the following steps: S1. Obtain the three-dimensional point cloud coordinate set of the entire appearance of the dam body; S1.

1. Use drone visual photography and laser radar equipment to obtain the dam surface image and laser coordinate data of the entire dam body. Use the method of matching adjacent image feature points and verifying through laser radar to process the captured global dam surface image and obtain the 3D point cloud coordinate dataset A of the entire dam surface at time t. t ={x ti ,y ti ,z ti }, where i = 1, 2, 3, ..., p, and p is the number of three-dimensional point cloud data representing the overall appearance features of the dam body; S1.

2. According to the spatial position of the dam surface, the dam surface is evenly divided into m dam surface fitting areas. The three-dimensional point cloud coordinate dataset B of the entire dam surface is t Divided into {B t1 ,B t2 ,B t3 ,…,B tm }, where B tm It represents the three-dimensional point cloud coordinate data set of the dam surface fitting area numbered m at time t; S2, the three-dimensional point cloud coordinate set B of the entire dam surface at time t t Perform multivariate regression statistical analysis to determine the multivariate continuous function set C of the entire dam surface t ={f t1 (x,y,z),f t2 (x,y,z),f t3 (x,y,z),…,f tm (x,y,z)}, multivariate continuous function set C t The calculation steps are as follows: S2.1, based on the 3D point cloud coordinate dataset B of the entire dam surface tm , solve the multivariate continuous function f of each dam surface fitting area separately tm (x, y, z) and the coefficient of determination R of each dam surface fitting area 2 : 1) Assume that the dam surface multivariate continuous function f tm The (x,y,z) model is: Among them, a jk is the function coefficient, j is the number of terms in x, and k is the number of terms in y; 2) Use the residual sum of squares S(x,y,z) as the evaluation function and substitute it into the coordinate set B tm , and set the restriction function at the same time: Solution The optimal coefficient solution set {a 00 ,a 01 ,a 02 ,a 03 ,a 10 ,...,a jk }, we get the multivariate continuous function f of each dam surface fitting area at the current time t tm (x,y,z); 3) Calculate the multivariate continuous function f of each dam surface fitting area tm Coefficient of determination of (x,y,z) Among them, SSR is the regression sum of squares, and SST is the total sum of squares; S2.

2. Determine the coefficient R of each dam surface fitting area 2 Is it greater than 0.9? If the coefficient of determination R 2 >0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) meets the accuracy requirement, and execute step S2.4; If the coefficient of determination R 2 ≤0.9, indicating that the dam surface fitting area continuous function f tm (x, y, z) does not meet the accuracy requirement, and execute step S2.3; S2.

3. For continuous functions f that do not meet the accuracy requirements tm (x, y, z) coordinate set B of the dam surface fitting area tm , based on the geometric construction, the dam surface fitting area is divided into four equal parts, then B tm It consists of four sub-areas {B t(m1) ,B t(m2) ,B t(m3) ,B t(m4) }, perform step S2.1 fitting and solving on the re-divided coordinate set area. If the determination coefficient R of the re-fitted multivariate continuous function is 2 If it is still not greater than 0.9, the coordinate set of the dam surface fitting area that still does not meet the accuracy requirement after division is further divided into four equal parts until the determination coefficient R of the multivariate continuous function of all dam surface fitting areas within the dam surface is 2 If both are greater than 0.9, stop dividing; At this time, the mth dam surface fitting area B tm There are b (b ≥ 1) sub-regions {B t(m1) ,B t(m2) ,B t(m3) ,…,B t(mb) }, each sub-region has a high-precision multivariate continuous function f tmb (x,y,z); S2.4 When all sub-regions in m regions have a determination coefficient R 2 When all are greater than 0.9, there are a total of n multivariate continuous functions on the dam surface at time t; S3, the three-dimensional point cloud coordinate data set B of the entire dam surface at time t+1 (t+1)m Repeat the above steps S1 and S2 to determine the multivariate continuous function set C of the entire dam surface at time t+1 t+1 ={f (t+1)1 (x,y,z),f (t+1)2 (x,y,z),f (t+1)3 (x,y,z),…,f (t+1)m (x, y, z)}, there are a total of l multivariate continuous functions on the dam surface at time t+1; S4, compare the multivariate continuous function set C of the entire dam surface at time t t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 , determine whether there is any deformation on the dam surface, and divide the warning level according to the changes.

2. The method for monitoring and early warning of global deformation of dam appearance according to claim 1 is characterized by: After obtaining the 3D point cloud coordinate dataset A of the entire appearance of the dam body t Then, the deformation monitoring data of the dam part and / or a certain dam section obtained by traditional measurement methods are used to measure the three-dimensional point cloud coordinate data set A. t Correction is performed to obtain the corrected 3D point cloud coordinate data set B of the entire dam surface t ={x i ,y i ,z i }, where i=1,2,3,…,p.

3. The method for monitoring and early warning the deformation of the dam appearance in the whole area according to claim 1 or 2 is characterized in that: The step S4 compares the multivariate continuous function set C of the entire dam surface at time t. t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 , determine whether the dam surface is deformed, and divide the warning level according to the changes. The specific methods are as follows: (1) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 The number of functions is equal, that is, n = l, and the function model in the same area has not changed, indicating that the dam surface has not deformed and no early warning is needed; (2) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 The number of functions is equal, that is, n = l, but the function model in the same area changes, and a first-level warning is issued for the changed area based on the dam surface area number; (3) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and (ln) / 4≤2, it means that the dam surface is partially deformed, and a second-level warning is issued for the changed area according to the dam surface area number; (4) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and 2<(ln) / 4≤5, it means that deformation occurs in many places on the dam surface, and a three-level warning is issued for the changed area according to the dam surface area number; (5) At time t, the multivariate continuous function set C of the entire dam surface t and the multivariate continuous function set C of the entire dam surface at time t+1 t+1 When the number of functions is not equal, that is, n≠l, and 5<(ln) / 4, it means that the dam surface is seriously deformed, and a fourth-level warning is issued for the changed area according to the dam surface area number.

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