A settlement warning method and warning management system for large-scale hydraulic structures

Abstract

The present invention discloses a large-size water conservancy building settlement warning method and a warning management system, which relate to the field of water conservancy supervision. The present invention includes obtaining the settlement detection gradient value of each position point according to the difference between the settlement detection value of each position point and that of the adjacent position point; dividing the building body into several common settlement areas according to the relative positions between each position point, the stress detection value and the settlement detection gradient value of each position point; obtaining 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; judging whether the actual settlement amount of each position point exceeds the corresponding settlement warning value; if so, taking the position point whose actual settlement amount exceeds the corresponding settlement warning value as a warning position point, and sending out a settlement warning for the warning position point; if not, no settlement warning is carried out. The present invention improves the efficiency and accuracy of warning management.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy supervision, and particularly relates to a settlement warning method and a warning management system for large-sized water conservancy buildings. Background Art

[0002] With the rapid development of water conservancy project construction in China, large-sized 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 various factors such as geological conditions, construction quality, and operation management, settlement phenomena will inevitably occur during the construction and operation of large-sized water conservancy buildings. 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 regular measurements, which have problems such as long monitoring periods, untimely data acquisition, and low automation levels, and are difficult to meet the real-time, dynamic, and accurate settlement monitoring requirements of large-sized water conservancy buildings. Summary of the Invention

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

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] The present invention provides a settlement warning method for large-sized water conservancy buildings, including,

[0007] Obtaining the settlement warning values at multiple position points of the building body;

[0008] Obtaining the stress detection values and settlement detection values at multiple position points of the building body;

[0009] Obtaining the settlement detection gradient value of each position point according to the difference between the settlement detection value of each position point and that of the adjacent position point;

[0010] Dividing the building body into several common settlement areas according to the relative positions between each position point and the stress detection values and settlement detection gradient values of each position point;

[0011] Obtaining 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;

[0012] Judging whether the actual settlement amount of each position point exceeds the corresponding settlement warning value;

[0013] If so, the position points where the actual settlement amount exceeds the corresponding settlement warning value are used as warning position points, and a settlement warning for the warning position points is issued;

[0014] If not, no settlement warning is carried out.

[0015] The present invention also discloses a settlement warning method for large-sized hydraulic buildings, including,

[0016] Obtaining a digital sand table containing the building body;

[0017] Receiving a settlement warning for the warning position points;

[0018] Highlighting the warning position points in the digital sand table.

[0019] The present invention also discloses a settlement warning management system for large-sized hydraulic buildings, including,

[0020] A position sensor for collecting settlement detection values of multiple position points of the building body;

[0021] A strain gauge for collecting stress detection values of multiple position points of the building body;

[0022] An alarm analysis unit for obtaining settlement warning values of multiple position points of the building body;

[0023] Obtaining stress detection values and settlement detection values of multiple position points of the building body;

[0024] Obtaining the settlement detection gradient value of each position point according to the difference between the settlement detection value of each position point and that of the adjacent position points;

[0025] Dividing the building body into several common settlement areas according to the relative positions between each position point and the stress detection values and settlement detection gradient values of each position point;

[0026] Obtaining 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;

[0027] Judging whether the actual settlement amount of each position point exceeds the corresponding settlement warning value;

[0028] If so, the position points where the actual settlement amount exceeds the corresponding settlement warning value are used as warning position points, and a settlement warning for the warning position points is issued;

[0029] If not, no settlement warning is carried out;

[0030] An alarm display unit for obtaining a digital sand table containing the building body;

[0031] Receiving a settlement warning for the warning position points;

[0032] Highlight the early warning position points in the digital sand table.

[0033] The present invention detects the stress and settlement detection amounts at different positions of a building through a position sensor and a strain gauge. And through an alarm analysis unit, the stress and settlement amounts at multiple points of the building are coupled and analyzed to distinguish different common settlement areas, and the accurate settlement values at different positions of the building are automatically and accurately obtained, improving the efficiency and accuracy of early warning management.

[0034] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0036] Figure 1 Schematic diagram of the functional units and information flow directions of a large-size water conservancy building settlement early warning management system according to an embodiment of the present invention;

[0037] Figure 2 Schematic diagram of the step flow of the alarm analysis unit according to an embodiment of the present invention;

[0038] Figure 3 Schematic diagram of the step flow of the alarm display unit according to an embodiment of the present invention;

[0039] Figure 4 Schematic diagram of the step flow of step S3 according to an embodiment of the present invention;

[0040] Figure 5 Schematic diagram of the step flow of step S4 according to an embodiment of the present invention;

[0041] Figure 6 Schematic diagram of the step flow of step S41 according to an embodiment of the present invention Figure 1 ;

[0042] Figure 7 Schematic diagram of the step flow of step S41 according to an embodiment of the present invention Figure 2 ;

[0043] Figure 8 Schematic diagram of the step flow of step S415 according to an embodiment of the present invention;

[0044] In the drawings, the list of components represented by each reference numeral is as follows:

[0045] 1 - Position sensor, 2 - Strain gauge, 3 - Alarm analysis unit, 4 - Alarm display unit. Specific implementation mode

[0046] To make the objectives, technical solutions and advantages of this application clearer, the following will further describe the implementation modes of this application in detail with reference to the accompanying drawings.

[0047] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. The implementation modes described in the following exemplary embodiments do not represent all implementation modes consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] Please refer to 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 amount detection achieved by using laser and radio positioning technologies. The strain gauge 2 can be arranged on the surface of the building body in a pasted - type manner or in a drilled - type manner inside the building body.

[0049] In this solution, the building body is taken as a dam as an example. Of course, in actual applications, it can also be other water conservancy buildings, such as sluices, ship locks, water transportation interchanges, etc. During the operation of this early - warning system, first, the position sensor 1 and the strain gauge 2 respectively execute step S01 and step S02 to collect the settlement detection values and stress detection values of multiple position points on the dam. The position points in this solution can be evenly distributed on the dam body, or on this basis, the position points for adding sensors 1 and strain gauges 2 can be increased for key components.

[0050] Since the settlement amount of the dam is usually extremely small, it is very difficult to achieve sufficient high - precision settlement amount detection even by using high - precision laser, radio, and satellite positioning. Therefore, this solution combines the detection of the dam body stress to achieve the division of different settlement areas of the dam body. This is because the materials used to build the dam are not completely ideal rigid body structures. Therefore, even though the large - scale dam is an integral building, there will be settlement areas with different settlement states. The settlement of position 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 amount of the dam, the detection of stress is simpler and more accurate. Therefore, the combination of stress and settlement amount detection can be used to accurately divide different settlement areas of the dam, and on this basis, calculate the settlement of different positions of the dam.

[0051] Please refer to Figure 2 As shown, in the specific analysis process, first, the alarm analysis unit 3 executes step S1 to obtain the settlement warning values of multiple position points of the dam. This is because the settlement requirements for different positions of the dam are different. For example, for the main building of the dam, that is, the settlement control requirement for the dam body is very high, but for the settlement requirements of auxiliary buildings such as the sump and energy dissipation facilities, they are slightly lower. Next, step S2 can be executed to obtain the stress detection values and settlement detection values of multiple position points of the dam.

[0052] Please refer to Figure 2 and 4 As shown, after receiving the settlement amount and stress detections, next, step S3 can be executed to obtain the settlement detection gradient value of each position point based on the difference between the settlement detection values of each position point and its adjacent position points. In practical applications, for each position point, the ratio of the difference between the settlement detection values of this position point and the adjacent position point with the closest distance to the distance can be used as the settlement detection gradient value of this position point.

[0053] Since the dam is a three-dimensional structure and each position point has spatial anisotropy, in order to fully consider the differences with other position points in different directions, for each position point, first, step S31 can be executed to evenly divide the space where this 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 closest to this position point in each quadrant as the target adjacent position point of this position point. Next, step S33 can be executed to calculate the average value of the ratio of the difference between the settlement detection values of this position point and the target adjacent position points in each quadrant to the corresponding distance as the settlement detection gradient value of this position point.

[0054] To supplement the implementation process of the above steps S31 to S33, the source code of some functional modules is provided, and corresponding explanations are given in the comment section. To avoid the leakage of data involving trade secrets, some data that does not affect the implementation of the solution is desensitized. The same applies hereinafter.

[0055] #include <iostream>

[0056] #include <vector>

[0057] #include <cmath>

[0058] #include <limits>

[0059] / / Define the structure of the position point, including coordinates and settlement detection value

[0060] struct Point {

[0061] double x, y; / / Coordinates of the position point

[0062] double settlementValue; / / Settlement detection value

[0063] double gradientValue; / / Settlement detection gradient value

[0064] };

[0065] / / Define the enumeration type of quadrants

[0066] enum Quadrant {

[0067] Q1, / / First quadrant (x > 0, y > 0)

[0068] Q2, / / Second quadrant (x < 0, y > 0)

[0069] Q3, / / Third quadrant (x < 0, y < 0)

[0070] Q4 / / Fourth quadrant (x > 0, y < 0)

[0071] };

[0072] / / Calculate the distance between two points

[0073] double calculateDistance(const Point& p1, const Point& p2) {

[0074] return std::sqrt(std::pow(p1.x - p2.x, 2) + std::pow(p1.y - p2.y, 2));

[0075] }

[0076] / / Determine the quadrant of the target point relative to the reference point

[0077] Quadrant getQuadrant(const Point& reference, const Point& target) {

[0078] double dx = target.x - reference.x;

[0079] double dy = target.y - reference.y;

[0080] if (dx > 0 && dy > 0) return Q1;

[0081] if (dx < 0 && dy > 0) return Q2;

[0082] if (dx < 0 && dy < 0) return Q3;

[0083] if (dx > 0 && dy < 0) return Q4;

[0084] return Q1; / / Default return the first quadrant (when dx or dy is 0)

[0085] }

[0086] / / Calculate the settlement detection gradient value for each position point

[0087] void calculateGradient(std::vector <point>&points) {

[0088] for (size_t i = 0; i<points.size(); ++i) {

[0089] Point¤tPoint = points[i];

[0090] std::vector <point>nearestNeighbors(4); / / Store the nearest neighbor points in each quadrant

[0091] std::vector <double>minDistances(4, std::numeric_limits <double>::max()); / / Store the minimum distance for each quadrant

[0092] / / Traverse all points to find the nearest neighbor in each quadrant

[0093] for (size_t j = 0; j < points.size(); ++j) {

[0094] if (i == j) continue; / / Skip itself

[0095] Point& neighborPoint = points[j];

[0096] double distance = calculateDistance(currentPoint, neighborPoint);

[0097] Quadrant quadrant = getQuadrant(currentPoint, neighborPoint);

[0098] / / If the current point is closer, update the nearest neighbor

[0099] if (distance < minDistances[quadrant]) {

[0100] minDistances[quadrant] = distance;

[0101] nearestNeighbors[quadrant] = neighborPoint;

[0102] }

[0103] }

[0104] / / Calculate the settlement detection gradient value

[0105] double sumGradient = 0.0;

[0106] int validQuadrants = 0;

[0107] for (int q = 0; q < 4; ++q) {

[0108] if (minDistances[q] != std::numeric_limits <double>::max()) { / / If there are adjacent points within the quadrant

[0109] double diff = std::abs(currentPoint.settlementValue - nearestNeighbors[q].settlementValue);

[0110] sumGradient += diff / minDistances[q]; / / Divide the difference by the distance

[0111] validQuadrants++;

[0112] }

[0113] }

[0114] if (validQuadrants > 0) {

[0115] currentPoint.gradientValue = sumGradient / validQuadrants; / / Calculate the mean value

[0116] } else {

[0117] currentPoint.gradientValue = 0.0; / / The gradient value is 0 when there are no adjacent points

[0118] }

[0119] }

[0120] }

[0121] int main() {

[0122] / / Example data: There are 5 position points in the example

[0123] std::vector <point>points = {

[0124] {0.0, 0.0, 10.0, 0.0},

[0125] {1.0, 1.0, 12.0, 0.0},

[0126] {-1.0, 1.0, 8.0, 0.0},

[0127] {-1.0, -1.0, 9.0, 0.0},

[0128] {1.0, -1.0, 11.0, 0.0}

[0129] };

[0130] / / Calculate the settlement detection gradient value for each position point

[0131] calculateGradient(points);

[0132] / / Output the result

[0133] for (const auto&point : points) {

[0134] std::cout << "Position point: (" << point.x << ", " << point.y << "), Settlement detection gradient value: " << point.gradientValue << std::endl;

[0135] }

[0136] return 0;

[0137] }

[0138] 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 a target adjacent position point closest in distance is selected within each quadrant. Then, the difference between the settlement detection value of this position point and that of the target adjacent position point 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 point, and finally calculates the gradient value.

[0139] Please refer to Figure 2 and 5 As shown in FIGS. 0 to 8, after obtaining the settlement detection gradient values of each monitoring point, the following step S4 can be executed. According to the relative positions between each position point, as well as the stress detection values and settlement detection gradient values of each position point, the dam is divided into several common settlement areas. Specifically, first, step S41 can be executed to obtain several position point combinations composed of position points with consistent confined settlement states according to the stress detection values and settlement detection gradient values of each position point. Specifically, first, step S411 can be executed. The cumulative value of the differences between the stress detection values and settlement detection gradient values between 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 dam is calculated and obtained. Next, step S412 can be executed to select multiple position points as reference position points among 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 points, it is classified into the same position point combination as the reference position point with the smallest state asynchronous rate, and several position point combinations are obtained.

[0140] However, whether the position points within each obtained position point combination are completely consistent in the confined settlement state has not been verified. Therefore, next, step S415 can be executed to determine whether the confined settlement states between each position point within the position point combination are consistent. In specific operations, first, step S4151 can be executed. Within each position point combination, the mean values of the stress detection values and settlement detection gradient values of all the position points therein are calculated and obtained. Next, step S4152 can be executed to determine whether the position point with the smallest state asynchronous rate with the mean values of the stress detection values and settlement detection gradient values of all the position points within each position point combination is the reference position point within the position point combination. If so, then step S4153 can be executed to determine that there is consistency, and step S416 can be executed to obtain several position point combinations composed of position points with consistent confined settlement states. If not, then step S4154 can be executed to determine that there is no consistency, and next, step S417 can be executed to re-select the reference position points, which can be the position point with the smallest state asynchronous rate with the mean values of the stress detection values and settlement detection gradient values of all the position points within each position point combination as the updated reference position point. Then, return to execute steps S411 to S415 to divide and obtain the updated position point combinations, and continuously determine whether the confined settlement states between each position point within the updated position point combinations are consistent. The above method ensures the consistency within the output position point combinations through iterative verification. Of course, in practice, it does not need to be completely consistent. Therefore, certain accuracy conditions can be set to avoid reducing the grouping efficiency of position points due to long-term iterative calculations.

[0141] To supplement the implementation process of the above steps S411 to S414, the source code of some functional modules is provided, and comparative explanatory notes are given in the comment section.

[0142] #include <iostream>

[0143] #include <vector>

[0144] #include <cmath>

[0145] #include <limits>

[0146] #include <algorithm>

[0147] / / Define the position point structure, including coordinates, stress detection value, and settlement detection gradient value

[0148] struct Point {

[0149] double x, y; / / Coordinates of the position point

[0150] double stressValue; / / Stress detection value

[0151] double gradientValue; / / Settlement detection gradient value

[0152] int clusterId; / / ID of the position point combination it belongs to

[0153] };

[0154] / / Calculate the state asynchronous rate between two position points

[0155] double calculateAsyncRate(const Point& p1, const Point& p2) {

[0156] double stressDiff = std::abs(p1.stressValue - p2.stressValue);

[0157] double gradientDiff = std::abs(p1.gradientValue - p2.gradientValue);

[0158] return stressDiff + gradientDiff; / / The state asynchronous rate is the sum of the stress difference and the gradient difference

[0159] }

[0160] / / Initialize the reference position point

[0161] std::vector <int>initializeCenters(const std::vector <point>&points, int k) {

[0162] std::vector <int>centers;

[0163] centers.push_back(0); / / The first point in the example is the reference point

[0164] for (int i = 1; i < k; ++i) {

[0165] int farthestPoint = -1;

[0166] double maxDistance = -1.0;

[0167] for (size_t j = 0; j < points.size(); ++j) {

[0168] double minDistance = std::numeric_limits <double>::max();

[0169] for (int center : centers) {

[0170] double distance = calculateAsyncRate(points[j], points[center]);

[0171] if (distance <minDistance) {

[0172] minDistance = distance;

[0173] }

[0174] }

[0175] if (minDistance>maxDistance) {

[0176] maxDistance = minDistance;

[0177] farthestPoint = j;

[0178] }

[0179] }

[0180] centers.push_back(farthestPoint); / / Select the farthest point as the new reference point

[0181] }

[0182] return centers;

[0183] }

[0184] / / Divide the position point combination

[0185] void clusterPoints(std::vector <point>&points, const std::vector <int>¢ers) {

[0186] for (size_t i = 0; i<points.size(); ++i) {

[0187] double minAsyncRate = std::numeric_limits <double>::max();

[0188] int bestCluster = -1;

[0189] for (size_t j = 0; j < centers.size(); ++j) {

[0190] double asyncRate = calculateAsyncRate(points[i], points[centers[j]]);

[0191] if (asyncRate < minAsyncRate) {

[0192] minAsyncRate = asyncRate;

[0193] bestCluster = j;

[0194] }

[0195] }

[0196] points[i].clusterId = bestCluster; / / Assign the point to the cluster where the reference point with the minimum state asynchronous rate is located

[0197] }

[0198] }

[0199] / / Update the reference position points

[0200] bool updateCenters(std::vector <point>&points, std::vector <int>¢ers) {

[0201] bool changed = false;

[0202] for (size_t i = 0; i<centers.size(); ++i) {

[0203] double minAsyncRate = std::numeric_limits <double>::max();

[0204] int bestCenter = -1;

[0205] double sumStress = 0.0, sumGradient = 0.0;

[0206] int count = 0;

[0207] / / Calculate the mean values of the stress detection values and settlement detection gradient values for all points within the current combination

[0208] for (const auto&point : points) {

[0209] if (point.clusterId == static_cast <int>(i)) {

[0210] sumStress += point.stressValue;

[0211] sumGradient += point.gradientValue;

[0212] count++;

[0213] }

[0214] }

[0215] double avgStress = sumStress / count;

[0216] double avgGradient = sumGradient / count;

[0217] / / Find the point with the minimum asynchronous rate from the mean state as the new reference point

[0218] for (size_t j = 0; j<points.size(); ++j) {

[0219] if (points[j].clusterId == static_cast <int>(i)) {

[0220] double asyncRate = std::abs(points[j].stressValue - avgStress) +std::abs(points[j].gradientValue - avgGradient);

[0221] if (asyncRate<minAsyncRate) {

[0222] minAsyncRate = asyncRate;

[0223] bestCenter = j;

[0224] }

[0225] }

[0226] }

[0227] / / If the new reference point is different from the current reference point, update the reference point

[0228] if (bestCenter != centers[i]) {

[0229] centers[i] = bestCenter;

[0230] changed = true;

[0231] }

[0232] }

[0233] return changed;

[0234] }

[0235] int main() {

[0236] / / Example data: There are 6 position points in the example

[0237] std::vector <point>points = {

[0238] {0.0, 0.0, 30.0, 0.1, -1},

[0239] {1.0, 0.0, 35.0, 0.12, -1},

[0240] {0.0, 1.0, 32.0, 0.08, -1},

[0241] {1.0, 1.0, 40.0, 0.15, -1},

[0242] {2.0, 2.0, 50.0, 0.2, -1},

[0243] {2.0, 1.0, 45.0, 0.18, -1}

[0244] };

[0245] int n = 2; / / Example is divided into 2 position point combinations

[0246] std::vector <int>centers = initializeCenters(points, n); / / Initialize the reference position points

[0247] bool changed;

[0248] do {

[0249] clusterPoints(points, centers); / / Divide the position point combinations

[0250] changed = updateCenters(points, centers); / / Update the reference position points

[0251] } while (changed); / / Until the reference position points no longer change

[0252] / / Output the results

[0253] for (const auto&point : points) {

[0254] std::cout << "Position point: (" << point.x << ", " << point.y << "), Combination ID: " << point.clusterId << std::endl;

[0255] }

[0256] return 0;

[0257] }

[0258] 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 combination division by iteratively optimizing the reference position points.

[0259] Please continue to refer to Figure 2 and 5 As shown, after completing the classification of the consistency of the bearing settlement state 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. Step S42: Within each combination of position points, 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 combination of position points are distributed on the dam, thereby obtaining all the common settlement areas of the dam.

[0260] Please continue to refer to Figures 1 to 3 As shown, after completing the division of the common settlement areas, the settlement states of different position points within each common settlement area are consistent. Therefore, the following step S5 can be executed. Based on the settlement detection values of each position point within the same common settlement area, the actual detection value of each position point within each common settlement area can be obtained. In specific calculations, the average value of the settlement detection values of all position points within the same common settlement area can be used as the actual detection value of each position point within this common settlement area.

[0261] In the process of warning about the dam settlement, first, step S6 can be executed to determine whether the actual settlement amount of each position point exceeds the corresponding settlement warning value. If so, then step S7 can be executed next. The position points whose actual settlement amount exceeds the corresponding settlement warning value are regarded as warning position points, and a settlement warning for the warning position points is issued. If not, then step S8 can be executed next without issuing a settlement warning.

[0262] To facilitate the intuitive management of the dam settlement by the staff, the system also sets an alarm display unit 4. During operation, first, step S041 can be executed to obtain a digital sand table containing the dam. Next, step S042 can be executed to receive the settlement warning for the warning position points. Finally, step S043 can be executed to prominently display the warning position points within the digital sand table, which can be to highlight the warning position points or to be accompanied by sound and light alarms.

[0263] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, systems, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of an instruction, and the module, program segment, or part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks may also occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in the reverse order, depending on the functions involved.

[0264] It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding functions or actions, such as a circuit or an ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware, etc.

[0265] Although the present invention has been described in connection with the various embodiments, however, in the process of implementing the claimed invention, those skilled in the art can understand and realize other variations of the disclosed embodiments by viewing the accompanying 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 a plurality. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0266] The various embodiments of the present application have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the disclosed embodiments.< / 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 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 position point other than the reference position point, classify it and the reference position point with the smallest state asynchronous rate into the same position point combination to obtain multiple position point combinations; 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; Arrange the distribution areas of the location points adjacent to each other in each location point combination on the building to obtain the total common settlement area of ​​the building; 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 obtaining a combination of several position points consisting of position points having consistent pressure 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 combination of several position points consisting of position points with consistent pressure settlement states is 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.

4. The method according to claim 3, 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 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, it is deemed to be consistent; If not, it is deemed inconsistent.

5. The method according to claim 4, characterized in that The step of reselecting the reference position point includes: The position point with the smallest state asynchronous rate with the mean of stress detection value and settlement detection gradient value of all position points in each position point combination is taken as the updated reference position point.

6. 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.

7. 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 building as described in any one of claims 1 to 6; The warning location points are highlighted in the digital sandbox.

8. 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 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 position point other than the reference position point, classify it and the reference position point with the smallest state asynchronous rate into the same position point combination to obtain multiple position point combinations; In each position point combination, the area distributed on the building body of multiple position points adjacent to each other in position is regarded as a common settlement area according to the relative positions between each position point; Arrange the distribution areas of the location points adjacent to each other in each location point combination on the building to obtain the total common settlement area of ​​the building; 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.

Images