Large-size water conservancy building settlement early warning method and early warning management system

Abstract

The invention discloses a large-size water conservancy building settlement early warning method and an early warning management system, and relates to the field of water conservancy supervision. The method comprises the following steps: obtaining a settlement detection gradient value of each position point according to a difference value between settlement detection values of each position point and an adjacent position point; dividing the building body into a plurality of co-settlement areas according to the relative position between the position points and the stress detection value and the settlement detection gradient value of each position point; obtaining an actual detection value of each position point in each common settlement area according to the settlement detection value of each position point in the same common settlement area; whether the actual settlement amount of each position point exceeds the corresponding settlement early warning value or not is judged; if yes, the position point with the actual settlement amount exceeding the corresponding settlement early warning value serves as an early warning position point, and settlement early warning for the early warning position point is sent out; if not, settlement early warning is not carried out. According to the invention, the efficiency and accuracy of early warning management are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of water conservancy supervision, and in particular relates to a settlement early warning method and an early warning management system for large-scale water conservancy buildings. Background Art

[0002] With the rapid development of water conservancy engineering construction in my country, large-scale water conservancy buildings, such as large reservoirs, dams, sluice gates, etc., play an increasingly important role in the national economy and people's lives. However, due to the influence of geological conditions, construction quality, operation management and other factors, large-scale water conservancy buildings will inevitably produce settlement during construction and operation. Excessive settlement or uneven settlement will seriously affect the safety and stability of water conservancy buildings.

[0003] Traditional settlement monitoring methods mainly rely on manual periodic measurements, which have problems such as long monitoring cycle, untimely data acquisition, and low degree of automation. They are difficult to meet the needs of real-time, dynamic, and accurate settlement monitoring of large-scale water conservancy buildings. Summary of the invention

[0004] The purpose of the present invention is to provide a settlement early warning method and early warning management system for large-scale water conservancy buildings, which improves the efficiency and accuracy of early warning management by coupling analysis of stress and settlement at multiple points of the building.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides a large-scale water conservancy building settlement early warning method, comprising: Obtain settlement warning values ​​at multiple locations of the building; Obtain stress detection values ​​and settlement detection values ​​at multiple locations of the building; The settlement detection gradient value of each position point is obtained according to the difference between the settlement detection value of each position point and the settlement detection value of the adjacent position point; The building is divided into several common settlement areas according to the relative positions between each location point and the stress detection value and settlement detection gradient value of each location point; According to the settlement detection values ​​of each position point in the same common settlement area, the actual detection value of each position point in each common settlement area is obtained; Determine whether the actual settlement amount at each location point exceeds the corresponding settlement warning value; If so, the location point where the actual settlement exceeds the corresponding settlement warning value is taken as the warning location point, and a settlement warning is issued for the warning location point; If not, no subsidence warning will be issued.

[0006] The present invention also discloses a large-scale water conservancy building settlement early warning method, comprising: Obtain a digital sandbox containing a building; Receive settlement warnings for warning locations; The warning location points are highlighted in the digital sandbox.

[0007] The present invention also discloses a large-scale water conservancy building settlement early warning management system, comprising: Position sensor, used to collect settlement detection values ​​at multiple locations of the building; Strain gauges are used to collect stress detection values ​​at multiple locations of the building; An alarm analysis unit, used to obtain settlement warning values ​​at multiple locations of a building; Obtain stress detection values ​​and settlement detection values ​​at multiple locations of the building; The settlement detection gradient value of each position point is obtained according to the difference between the settlement detection value of each position point and the settlement detection value of the adjacent position point; The building is divided into several common settlement areas according to the relative positions between each location point and the stress detection value and settlement detection gradient value of each location point; According to the settlement detection values ​​of each position point in the same common settlement area, the actual detection value of each position point in each common settlement area is obtained; Determine whether the actual settlement amount at each location point exceeds the corresponding settlement warning value; If so, the location point where the actual settlement exceeds the corresponding settlement warning value is taken as the warning location point, and a settlement warning is issued for the warning location point; If not, no subsidence warning will be issued; An alarm display unit, used to obtain a digital sand table containing a building body; Receive settlement warnings for warning locations; The warning location points are highlighted in the digital sandbox.

[0008] The present invention detects stress and settlement detection amounts at different locations of a building through position sensors and strain gauges. The alarm analysis unit performs coupled analysis on stress and settlement amounts at multiple points of the building, distinguishes different common settlement areas, and automatically and accurately obtains accurate settlement values ​​at different locations of the building, thereby improving the efficiency and accuracy of early warning management.

[0009] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0011] Figure 1 A schematic diagram of functional units and information flows of a large-scale hydraulic building settlement early warning management system in one embodiment of the present invention; Figure 2 A schematic diagram of a step flow of an alarm analysis unit according to an embodiment of the present invention; Figure 3 A schematic diagram of a step flow of an alarm display unit according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the step flow of step S3 in one embodiment of the present invention; Figure 5 This is a schematic diagram of the step flow of step S4 in one embodiment of the present invention; Figure 6 The step S41 of the present invention is a schematic diagram of a step flow in an embodiment. Figure 1 ; Figure 7 The step S41 of the present invention is a schematic diagram of a step flow in an embodiment. Figure 2 ; Figure 8 This is a schematic diagram of a step flow chart of step S415 in an embodiment of the present invention; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1- Position sensor, 2- Strain gauge, 3- Alarm analysis unit, 4- Alarm display unit. DETAILED DESCRIPTION

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0013] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0014] See also Figures 1 to 3 As shown, the present invention provides a large-scale water conservancy building settlement early warning management system, which includes a position sensor 1, a strain gauge 2, an alarm analysis unit 3 and an alarm display unit 4 in terms of functional units. The position sensor 1 can be a high-precision settlement detection achieved by laser and radio positioning technology. The strain gauge 2 can be installed on the surface of the building body in an applied manner or in a drilled manner inside the building body.

[0015] The building in this scheme takes a dam as an example. Of course, in actual application, it can also be other water conservancy buildings, such as sluices, ship locks, water transport interchanges and other buildings. During the operation of this early warning system, the position sensor 1 and the strain gauge 2 first execute steps S01 and S02 respectively to collect the settlement detection values ​​and stress detection values ​​of multiple position points of the dam. The position points in this scheme can be evenly distributed on the dam body, and on this basis, additional position points for laying sensors 1 and strain gauges 2 can be added to key components.

[0016] Since the settlement of a dam is usually extremely small, it is difficult to detect the settlement with sufficiently high accuracy even with high-precision laser, radio, and satellite positioning. Therefore, this solution combines the detection of dam body stress to achieve the division of different settlement areas of the dam body. This is because the material used to build the dam is not a completely ideal rigid structure. Therefore, even if a large-scale dam is an integral building, there will be settlement areas with different settlement states. The settlement of points in the same settlement area is usually the same or similar, and the stress performance is also the same or similar. Compared with the settlement of a dam, the detection of stress is simpler and more accurate. Therefore, the dual detection of stress and settlement can be combined to achieve accurate division of different settlement areas of the dam, and on this basis, the settlement of different positions of the dam can be calculated.

[0017] See also Figure 2 As shown, in the specific analysis process, the alarm analysis unit 3 first executes step S1 to obtain the settlement warning values ​​of multiple locations of the dam. This is because the settlement requirements of different locations of the dam are different. For example, the settlement control requirements for the main building of the dam, that is, the dam body, are very high, but the settlement requirements for auxiliary buildings such as water collection wells and energy dissipation facilities are slightly lower. Next, step S2 can be executed to obtain the stress detection values ​​and settlement detection values ​​of multiple locations of the dam.

[0018] See also Figure 2 and 4 As shown, after receiving the settlement amount and stress detection, step S3 can be executed next to obtain the settlement detection gradient value of the position point according to the difference between the settlement detection value of each position point and the adjacent position point. In practical applications, for each position point, the ratio of the difference between the settlement detection value of the position point and the position point with the closest adjacent distance to the distance can be used as the settlement detection gradient value of the position point.

[0019] Since the dam is a three-dimensional structure, each position point has spatial isotropy. In order to fully consider the differences with other position points in different directions, for each position point, step S31 can be first executed to evenly divide the space where the position point is located into multiple quadrants. If the strain gauge 2 is laid on the surface of the dam body, it is divided into 4 spatial quadrants based on the plane where the strain gauge 2 is laid. If the strain gauge 2 is set inside the dam body, it is evenly divided into 8 spatial quadrants with the position where the strain gauge 2 is placed as the origin. Next, step S32 can be executed to select an adjacent position point in each quadrant that is closest to the position point as the target adjacent position point of the position point. Next, step S33 can be executed to calculate the difference between the settlement detection value of the position point and the target adjacent position point in each quadrant and the average of the corresponding distance ratio as the settlement detection gradient value of the position point.

[0020] In order to supplement the implementation process of the above steps S31 to S33, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0021] #include <iostream> #include <vector> #include <cmath> #include <limits> / / Define the location point structure, including coordinates and settlement detection values struct Point { double x, y; / / coordinates of the position point double settlementValue; / / settlement detection value double gradientValue; / / Sedimentation detection gradient value }; / / Define the quadrant enumeration type enum Quadrant { Q1, / / First quadrant (x>0, y>0) Q2, / / Second quadrant (x<0, y>0) Q3, / / The third quadrant (x<0, y<0) Q4 / / Fourth quadrant (x>0, y<0) }; / / Calculate the distance between two points double calculateDistance(const Point&p1, const Point&p2) { return std::sqrt(std::pow(p1.x - p2.x, 2) + std::pow(p1.y - p2.y,2)); } / / Judge the quadrant of the target point relative to the reference point Quadrant getQuadrant(const Point&reference, const Point&target) { double dx = target.x - reference.x; double dy = target.y - reference.y; if (dx>0&&dy>0) return Q1; if (dx<0&&dy>0) return Q2; if (dx<0&&dy<0) return Q3; if (dx>0&&dy<0) return Q4; return Q1; / / Default returns to the first quadrant (when dx or dy is 0) } / / Calculate the settlement detection gradient value at each location point void calculateGradient(std::vector <point>&points) { for (size_t i = 0; i<points.size(); ++i) { Point¤tPoint = points[i]; std::vector <point>nearestNeighbors(4); / / Store the nearest neighbor points in each quadrant std::vector <double>minDistances(4, std::numeric_limits <double>::max()); / / Store the minimum distance of each quadrant / / Traverse all points and find the nearest neighbor point in each quadrant for (size_t j = 0; j <points.size(); ++j) { if (i == j) continue; / / skip itself Point&neighborPoint = points[j]; double distance = calculateDistance(currentPoint, neighborPoint); Quadrant quadrant = getQuadrant(currentPoint, neighborPoint); / / If the current point is closer, update the nearest neighbor if (distance <minDistances[quadrant]) { minDistances[quadrant] = distance; nearestNeighbors[quadrant] = neighborPoint; } } / / Calculate the gradient value of sedimentation detection double sumGradient = 0.0; int validQuadrants = 0; for (int q = 0; q<4; ++q) { if (minDistances[q] != std::numeric_limits <double>::max()) { / / If there are adjacent points in the quadrant double diff = std::abs(currentPoint.settlementValue -nearestNeighbors[q].settlementValue); sumGradient += diff / minDistances[q]; / / Difference divided by distance validQuadrants++; } } if (validQuadrants>0) { currentPoint.gradientValue = sumGradient / validQuadrants; / / Calculate the mean } else { currentPoint.gradientValue = 0.0; / / The gradient value is 0 when there is no neighboring point } } } int main() { / / Sample data: The sample has 5 locations std::vector <point>points = { {0.0, 0.0, 10.0, 0.0}, {1.0, 1.0, 12.0, 0.0}, {-1.0, 1.0, 8.0, 0.0}, {-1.0, -1.0, 9.0, 0.0}, {1.0, -1.0, 11.0, 0.0} }; / / Calculate the settlement detection gradient value for each position point calculateGradient(points); / / Output the result for (const auto&point : points) { std::cout << "Position point: (" << point.x << ", " << point.y << "), Settlement detection gradient value: " << point.gradientValue << std::endl; } return 0; } This code implements the steps to calculate the settlement detection gradient value for each position point. For each position point, the space where it is located is evenly divided into four quadrants, and the nearest adjacent position point in each quadrant is selected as the target adjacent position point. Then, the difference between the settlement detection value of this position point and the target adjacent position points in each quadrant is calculated and divided by the corresponding distance, and finally the average value is taken as the settlement detection gradient value of this position point. The code represents the quadrants through an enumeration type, uses distance calculation and conditional judgment to find the nearest neighbor points, and finally calculates the gradient value.

[0022] Please refer to Figure 2 and 5 As shown in FIG8, after obtaining the settlement detection gradient value of each monitoring point, step S4 can be executed next to divide the dam into several common settlement areas according to the relative position between each position point and the stress detection value and settlement detection gradient value of each position point. Specifically, step S41 can be executed first to obtain several position point combinations consisting of position points with consistent pressure settlement state according to the stress detection value and settlement detection gradient value of each position point. Specifically, step S411 can be executed first to use the accumulated value of the difference between the stress detection value and the settlement detection gradient value between two position points as the state asynchronous rate between the two position points, and calculate the state asynchronous rate between each position point of the dam. Next, step S412 can be executed to select multiple position points as reference position points from all the position points of the dam. Next, step S413 can be executed to obtain the state asynchronous rate between each reference position point and other position points according to the state asynchronous rate between each position point of the dam. Next, step S414 can be executed for each other position point other than the reference position point, and it and the reference position point with the smallest state asynchronous rate are classified into the same position point combination to obtain multiple position point combinations.

[0023] However, whether the position points in each position point combination obtained at this time have the consistency of the pressure settlement state has not been verified, so step S415 can be executed next to determine whether the pressure settlement state between each position point in the position point combination is consistent. In the specific operation, step S4151 can be executed first in each position point combination to calculate and obtain the mean of the stress detection value and the settlement detection gradient value of all the position points. Next, step S4152 can be executed to determine whether the position point with the smallest state asynchronous rate with the mean of the stress detection value and the settlement detection gradient value of all the position points in each position point combination is the reference position point in the position point combination. If so, step S4153 can be executed next to determine that there is consistency, and step S416 can be executed to obtain several position point combinations composed of position points with consistent pressure settlement state. If not, step S4154 can be executed next to determine that there is no consistency, and step S417 can be executed next to reselect the reference position point, which can be the position point with the smallest state asynchronous rate with the mean of the stress detection value and the settlement detection gradient value of all the position points in each position point combination as the updated reference position point. Then, the steps S411 to S415 are returned to obtain the updated position point combination, and the pressure settlement state between each position point in the updated position point combination is continuously determined to be consistent. The above method ensures the consistency within the output position point combination through iterative verification. Of course, in practice, it does not need to be completely consistent, so a certain accuracy condition can be set to avoid long-term iterative calculations that reduce the efficiency of grouping position points.

[0024] In order to provide additional explanation for the implementation process of the above-mentioned steps S411 to S414, source codes of some functional modules are provided, and comparative explanations are provided in the comments.

[0025] #include <iostream> #include <vector> #include <cmath> #include <limits> #include <algorithm> / / Define the location point structure, including coordinates, stress detection values ​​and settlement detection gradient values struct Point { double x, y; / / coordinates of the position point double stressValue; / / stress detection value double gradientValue; / / Sedimentation detection gradient value int clusterId; / / ID of the location point combination }; / / Calculate the state asynchronous rate between two position points double calculateAsyncRate(const Point&p1, const Point&p2) { double stressDiff = std::abs(p1.stressValue - p2.stressValue); double gradientDiff = std::abs(p1.gradientValue - p2.gradientValue); return stressDiff + gradientDiff; / / The state asynchronous rate is the accumulation of stress difference and gradient difference } / / Initialize the reference position std::vector <int>initializeCenters(const std::vector <point>&points,int k) { std::vector <int>centers; centers.push_back(0); / / The first point of the example is the reference point for (int i = 1; i <k; ++i) { int farthestPoint = -1; double maxDistance = -1.0; for (size_t j = 0; j <points.size(); ++j) { double minDistance = std::numeric_limits <double>::max(); for (int center : centers) { double distance = calculateAsyncRate(points[j], points[center]); if (distance <minDistance) { minDistance = distance; } } if (minDistance>maxDistance) { maxDistance = minDistance; farthestPoint = j; } } centers.push_back(farthestPoint); / / Select the farthest point as the new reference point } return centers; } / / Divide the position point combination void clusterPoints(std::vector <point>&points, const std::vector <int>¢ers) { for (size_t i = 0; i<points.size(); ++i) { double minAsyncRate = std::numeric_limits <double>::max(); int bestCluster = -1; for (size_t j = 0; j <centers.size(); ++j) { double asyncRate = calculateAsyncRate(points[i], points[centers[j]]); if (asyncRate <minAsyncRate) { minAsyncRate = asyncRate; bestCluster = j; } } points[i].clusterId = bestCluster; / / Assign points to the combination of benchmark points with the smallest state asynchronous rate } } / / Update the reference position bool updateCenters(std::vector <point>&points, std::vector <int>¢ers) { bool changed = false; for (size_t i = 0; i<centers.size(); ++i) { double minAsyncRate = std::numeric_limits <double>::max(); int bestCenter = -1; double sumStress = 0.0, sumGradient = 0.0; int count = 0; / / Calculate the mean of the stress detection value and settlement detection gradient value of all points in the current combination for (const auto&point : points) { if (point.clusterId == static_cast <int>(i)) { sumStress += point.stressValue; sumGradient += point.gradientValue; count++; } } double avgStress = sumStress / count; double avgGradient = sumGradient / count; / / Find the point with the smallest asynchronous rate with the mean state as the new reference point for (size_t j = 0; j <points.size(); ++j) { if (points[j].clusterId == static_cast <int>(i)) { double asyncRate = std::abs(points[j].stressValue - avgStress) +std::abs(points[j].gradientValue - avgGradient); if (asyncRate <minAsyncRate) { minAsyncRate = asyncRate; bestCenter = j; } } } / / If the new reference point is different from the current reference point, update the reference point if (bestCenter != centers[i]) { centers[i] = bestCenter; changed = true; } } return changed; } int main() { / / Sample data: The sample has 6 locations std::vector <point>points = { {0.0, 0.0, 30.0, 0.1, -1}, {1.0, 0.0, 35.0, 0.12, -1}, {0.0, 1.0, 32.0, 0.08, -1}, {1.0, 1.0, 40.0, 0.15, -1}, {2.0, 2.0, 50.0, 0.2, -1}, {2.0, 1.0, 45.0, 0.18, -1} }; int n = 2; / / The example is divided into 2 position point combinations std::vector <int>centers = initializeCenters(points, n); / / Initialize the reference position points bool changed; do { clusterPoints(points, centers); / / Divide the position point combinations changed = updateCenters(points, centers); / / Update the reference position points } while (changed); / / Until the reference position points no longer change / / Output the results for (const auto&point : points) { std::cout << "Position point: (" << point.x << ", " << point.y << "), Combination ID: " << point.clusterId << std::endl; } return 0; } This code implements the steps of dividing a dam into position point combinations with consistent bearing settlement states based on the stress detection values and settlement detection gradient values of each position point. First, calculate the state asynchronous rate between each position point and select multiple reference position points. Then, assign each position point to the combination where the reference position point with the minimum state asynchronous rate is located. Next, calculate the mean values of the stress detection values and settlement detection gradient values of the position points within each combination, and find the point with the minimum state asynchronous rate with the mean value as the new reference position point. If the reference position points change, re-divide the combinations until the reference position points no longer change. Finally, obtain several position point combinations with consistent bearing settlement states. The code ensures the accuracy of the combination division by iteratively optimizing the reference position points.

[0026] Please continue to refer to Figure 2 and 5 As shown, after completing the classification of the bearing settlement states of the position points, considering that the position points within the same common settlement area are an interconnected whole in space, the following steps can be executed. In step S42, within each position point combination, based on the relative positions between each position point, the area where multiple adjacent position points are distributed on the dam is regarded as the common settlement area. Next, step S43 can be executed to organize and obtain the areas where the adjacent position points within each position point combination are distributed on the dam to obtain all the common settlement areas of the dam.

[0027] Please continue reading Figures 1 to 3 As shown, after the division of the common settlement area is completed, the settlement states of different positions in each common settlement area are consistent, so step S5 can be performed next to obtain the actual detection value of each position point in each common settlement area according to the settlement detection values ​​of each position point in the same common settlement area. In the specific calculation, the average of the settlement detection values ​​of all the position points in the same common settlement area can be used as the actual detection value of each position point in the common settlement area.

[0028] In the process of early warning of dam settlement, step S6 can be first executed to determine whether the actual settlement amount of each location point exceeds the corresponding settlement warning value. If so, step S7 can be executed next to take the location point where the actual settlement amount exceeds the corresponding settlement warning value as the warning location point, and issue a settlement warning for the warning location point. If not, step S8 can be executed next to not issue a settlement warning.

[0029] In order to facilitate the staff to intuitively manage the dam settlement, the system is also provided with an alarm display unit 4. During operation, step S041 can be first executed to obtain a digital sand table containing the dam. Next, step S042 can be executed to receive a settlement warning for the warning position point. Finally, step S043 can be executed to highlight the warning position point in the digital sand table, which can be a highlight mark of the warning position point, or a sound or light alarm.

[0030] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0031] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0032] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0033] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.< / int> < / point> < / int> < / int> < / double> < / int> < / point> < / double> < / int> < / point> < / double> < / int> < / point> < / int> < / algorithm> < / limits> < / cmath> < / vector> < / iostream> < / point> < / double> < / double> < / double> < / point> < / point> < / limits> < / cmath> < / vector> < / iostream>

Claims

1. A method for early warning of settlement of large-scale water conservancy buildings, characterized in that: include, Obtain settlement warning values ​​at multiple locations of the building; Obtain stress detection values ​​and settlement detection values ​​at multiple locations of the building; The settlement detection gradient value of each position point is obtained according to the difference between the settlement detection value of each position point and the settlement detection value of the adjacent position point; The building is divided into several common settlement areas according to the relative positions between each location point and the stress detection value and settlement detection gradient value of each location point; According to the settlement detection values ​​of each position point in the same common settlement area, the actual detection value of each position point in each common settlement area is obtained; Determine whether the actual settlement amount at each location point exceeds the corresponding settlement warning value; If so, the location point where the actual settlement exceeds the corresponding settlement warning value is taken as the warning location point, and a settlement warning is issued for the warning location point; If not, no subsidence warning will be issued.

2. The method according to claim 1, characterized in that The step of obtaining the settlement detection gradient value of each position point according to the difference between the settlement detection value of each position point and the settlement detection value of the adjacent position point includes: For each location point, The space where the location point is located is evenly divided into multiple quadrants. In each quadrant, select an adjacent position point that is closest to the position point as the target adjacent position point of the position point. The average of the difference between the settlement detection value of the position point and the target adjacent position point in each quadrant and the corresponding distance ratio is calculated as the settlement detection gradient value of the position point.

3. The method according to claim 1, characterized in that The step of dividing the building into a plurality of common settlement areas according to the relative positions between each position point and the stress detection value and settlement detection gradient value of each position point, include, According to the stress detection value and the settlement detection gradient value of each position point, a plurality of position point combinations consisting of position points having consistent pressure settlement states are obtained; In each position point combination, the area distributed on the building body of multiple position points adjacent to each other in position is taken as a common settlement area according to the relative positions between each position point; The areas where the location points adjacent to each other in each location point combination are distributed on the building are sorted out to obtain the entire common settlement area of ​​the building.

4. The method according to claim 3, characterized in that The step of obtaining a combination of several position points consisting of position points with consistent pressure settlement states according to the stress detection value and the settlement detection gradient value of each position point, include, The accumulated value of the difference between the stress detection value and the settlement detection gradient value between the two position points is used as the state asynchronous rate between the two position points, and the state asynchronous rate between each position point of the building is calculated and obtained; Selecting a plurality of position points from all position points of the building as reference position points; According to the state asynchronous rate between each position point of the building body, the state asynchronous rate between each reference position point and other position points is obtained; For each location point other than the reference location point, it is classified into the same location point combination with the reference location point with the smallest state asynchronous rate, thereby obtaining a plurality of location point combinations.

5. The method according to claim 4, characterized in that The step of obtaining a combination of several position points having consistent pressure-bearing settlement states according to the stress detection value and the settlement detection gradient value of each position point also includes: Determine whether the pressure settlement state between each position point in the position point combination is consistent; If so, a plurality of position point combinations consisting of position points having consistent pressure settlement states are obtained; If not, the reference position point is reselected, the updated position point combination is obtained by division, and whether the pressure settlement state between each position point in the updated position point combination is consistent is continuously determined.

6. The method according to claim 5, characterized in that The step of judging whether the pressure settlement state between each position point in the position point combination is consistent, include, In each position point combination, the mean value of the stress detection value and the settlement detection gradient value of all the position points is calculated and obtained; Determine whether the position point with the smallest state asynchronous rate with the average of the stress detection value and the settlement detection gradient value of all the position points in each position point combination is the reference position point in the position point combination; If so, it is deemed to be consistent; If not, it is deemed inconsistent.

7. The method according to claim 6, characterized in that The step of reselecting the reference position point includes: The position point with the smallest state asynchronous rate with the average of the stress detection value and the settlement detection gradient value of all the position points in each position point combination is taken as the updated reference position point.

8. The method according to claim 1, characterized in that: The step of obtaining the actual detection value of each position point in each common settlement area according to the settlement detection value of each position point in the same common settlement area, include, In the same common settlement area, the average of the settlement detection values ​​of all the position points therein is taken as the actual detection value of each position point in the common settlement area.

9. A method for early warning of settlement of large-scale water conservancy buildings, characterized in that: include, Obtain a digital sandbox containing a building; Receiving a settlement warning for a warning location point in a settlement warning method for a large-scale hydraulic structure as described in any one of claims 1 to 8; The warning location points are highlighted in the digital sandbox.

10. A large-scale water conservancy building settlement early warning management system, characterized in that: include, Position sensor, used to collect settlement detection values ​​at multiple locations of the building; Strain gauges are used to collect stress detection values ​​at multiple locations of the building; An alarm analysis unit, used to obtain settlement warning values ​​at multiple locations of a building; Obtain stress detection values ​​and settlement detection values ​​at multiple locations of the building; The settlement detection gradient value of each position point is obtained according to the difference between the settlement detection value of each position point and the settlement detection value of the adjacent position point; The building is divided into several common settlement areas according to the relative positions between each location point and the stress detection value and settlement detection gradient value of each location point; According to the settlement detection values ​​of each position point in the same common settlement area, the actual detection value of each position point in each common settlement area is obtained; Determine whether the actual settlement amount at each location point exceeds the corresponding settlement warning value; If so, the location point where the actual settlement exceeds the corresponding settlement warning value is taken as the warning location point, and a settlement warning is issued for the warning location point; If not, no subsidence warning will be issued; An alarm display unit, used to obtain a digital sand table containing a building body; Receive settlement warnings for warning locations; The warning location points are highlighted in the digital sandbox.

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