Arch dam structure safety monitoring method and system

Abstract

The invention belongs to the field of dam safety monitoring, and particularly provides an arch dam structure safety monitoring method and system, and the method comprises the steps: obtaining monitoring data, comparing the monitoring data with corresponding early warning values, and recording the monitoring data as normal if all the monitoring data are smaller than the corresponding early warning values; otherwise, acquiring an early warning score of the abnormal measuring point, and executing a preset judgment process; the preset judgment process comprises the following steps: firstly checking measuring points of similar spaces, the same type and the same dam section, if the measuring points are abnormal at the same time, forming an early warning measuring point group, and calculating the similarity to determine a correlation weight coefficient; otherwise, directly calculating an early warning score. The early warning score is determined according to the abnormal rate weight coefficient, the association weight coefficient and the abnormal measuring point early warning score. And finally, according to a preset monitoring index system and the early warning score of the early warning measuring point group, determining the early warning score of the monitoring item, the sub-item and the object step by step. According to the technical scheme, the accuracy and reliability of safety monitoring of the arch dam structure can be improved, and more effective technical support is provided for safety monitoring stop of the arch dam structure.

Description

Technical Field

[0001] The present invention belongs to the field of dam safety monitoring, and in particular, relates to a method and system for monitoring the safety of an arch dam structure. Background Art

[0002] Arch dam structure safety monitoring refers to the use of various sensors arranged on the surface or inside the dam body to collect and analyze in real time the monitoring data such as stress, deformation, and seepage of the arch dam during operation, so as to assess the health status of the arch dam structure and timely discover and warn of potential safety risks.

[0003] As an integral spatial structure, an arch dam's various parts jointly bear loads in space. However, existing monitoring methods mostly focus on the analysis of a single measuring point, or simple statistical analysis of multiple measuring points, without adequate consideration of the spatial correlation between measuring points of different locations and types. On the other hand, existing evaluation index systems are usually divided according to monitoring type, such as deformation, stress, seepage, etc., without associating the monitoring points with the working performance of the arch dam. This makes it difficult to accurately determine the extent of the impact of abnormal conditions on the working performance of the dam once a monitoring point occurs. In particular, for dams such as arch dams with unique stress characteristics, the existing evaluation index system may not be able to fully reflect their safety status. In other words, the existing arch dam structure safety monitoring method cannot accurately and effectively monitor the arch dam structure, and there is still room for further improvement. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for monitoring the safety of an arch dam structure, which can improve the accuracy and reliability of the safety monitoring of the arch dam structure.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a method for monitoring the safety of an arch dam structure, comprising the following steps: The monitoring data related to arch dam stress monitoring, arch seat stability monitoring, and dam body overall stability monitoring are obtained respectively and compared with the corresponding warning values. If all monitoring data are less than the corresponding warning values, it is recorded as normal; otherwise, the warning score of the abnormal measuring point is obtained, and the preset judgment process is executed to obtain the warning score of the monitoring item, monitoring sub-item, and monitoring object, and then an early warning is issued according to the arch dam structure safety monitoring early warning level standard; The preset judgment process includes the following steps: First judgment step: Check the measuring points with similar spatial distance, the same type and the same dam section. If there are any abnormalities at the same time, they will form an early warning measuring point group and enter the second judgment step; The second judgment step: calculate the similarity between the warning measurement point groups, determine the correlation weight coefficient based on the similarity calculation result, and then enter the warning score calculation step; Warning score calculation step: determining the warning score of the warning measurement point group according to the abnormality rate weight coefficient, the correlation weight coefficient and the warning score of the abnormal measurement point; the abnormality rate weight coefficient is determined according to the number of measurement points in the warning measurement point group; Step-by-step determination steps: The warning scores of all warning measurement point groups are calculated through the warning score calculation step. According to the preset arch dam structure safety monitoring indicator system and the warning scores of the warning measurement point groups, the warning scores of the monitoring items, monitoring sub-items and monitoring objects are determined step by step.

[0006] In a preferred solution, the calculation of similarities between warning measurement point groups and the determination of association weight coefficients based on the similarity calculation results include: For any two measuring points between the early warning measuring point groups, a five-dimensional vector including spatial coordinates, time and monitoring data is compiled; After normalizing the five-dimensional vector, the corresponding similarity distance is calculated using the similarity distance calculation formula, and then the Manhattan distance is calculated. The comparison relationship between the Manhattan distance and the control threshold is used to determine the multidimensional correlation relationship between the early warning measurement point groups, thereby determining the correlation weight coefficient.

[0007] In a preferred solution, for any two measuring points between the early warning measuring point groups, a five-dimensional vector including spatial coordinates, time and monitoring data is compiled, including: For a measuring point A in a certain early warning measuring point group and a measuring point B in another early warning measuring point group, the five-dimensional vectors including spatial coordinates, time and monitoring data are compiled as follows: the vector corresponding to measuring point A L =( L 1, L 2, L 3. L 4. L 5), the vector corresponding to the measuring point A M =( M 1, M 2, M 3. M 4. M 5); in, L 1. L 2. L 3. L 4 and L 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point A; M 1. M 2. M 3. M 4 and M 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point B.

[0008] In a preferred solution, normalizing the five-dimensional vector includes: Using calculation formula and For vector L and vector M Perform normalization processing, where is a vector L No. i parameters, is a vector M No. j parameters, is a vector L No. i The normalized value of the parameters, is a vector M No. j The normalized value of the parameters, 、 are vectors L No. i The maximum and minimum values of the parameters, 、 are vectors M No. j The maximum and minimum values of the parameters.

[0009] In a preferred solution, the method of calculating the corresponding similarity distance using the similarity distance calculation formula and determining the multi-dimensional correlation between the warning measurement point groups by comparing the similarity distance with the control threshold value, thereby determining the correlation weight coefficient, includes: Using calculation formula Calculate the corresponding similarity distance value , for similarity distance values After normalization, the monitoring data time series of measuring points A and B are iterated step by step: ,in, k is the number of time series monitoring data, D It is the comprehensive Manhattan distance of the time series monitoring data of measuring point A and measuring point B; it defines the unit Manhattan distance of two time series monitoring data. for , then if the unit Manhattan distance of two time series monitoring data is If the control threshold value is within the range, it means that there is a multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.02; otherwise, it means that there is no multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.0.

[0010] In a preferred solution, determining the warning score of the warning measurement point group according to the abnormality rate weight coefficient, the correlation weight coefficient and the warning score of the abnormal measurement point includes: The warning score S of the warning measurement point group is calculated using the formula S=d×R×F; Where d is the abnormal rate weight coefficient. If the number of measuring points in the early warning measuring point group is 1, the value is 1.0; otherwise, the value is 1.02; R is the association weight coefficient. If there is no multidimensional association relationship between the early warning measuring point groups, the value is 1.0; otherwise, the value is 1.02; F is the early warning score of the abnormal measuring point.

[0011] In a preferred solution, the stepwise determination of the warning scores of monitoring items, monitoring sub-items and monitoring objects based on the preset arch dam structure safety monitoring index system and the warning scores of the warning measurement point group includes: The preset arch dam structure safety monitoring indicator system is divided into monitoring objects, monitoring sub-items, monitoring projects and monitoring measurement points from top to bottom; Among them, the warning score of the monitoring project is equal to the maximum warning score of the warning measurement point group; the warning score of the monitoring sub-item is determined by the maximum warning score of the monitoring items contained in the monitoring sub-item. Among them, if there are multiple monitoring items with warning scores greater than 0, the multi-item weight coefficient is 1.2, otherwise it is 1.0; the warning score of the monitoring object is determined based on the sum of the warning scores of all monitoring sub-items.

[0012] In a preferred solution, the monitoring object is a target arch dam structure; the monitoring sub-items include arch dam stress monitoring, arch seat stability monitoring and dam body overall stability monitoring, wherein the monitoring items corresponding to the monitoring sub-item of arch dam stress monitoring include strain monitoring, autogenous volume deformation monitoring and stress simulation; the monitoring items corresponding to the monitoring sub-item of arch seat stability monitoring include anti-three-dimensional deformation monitoring, dam abutment rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam body bedrock surface opening and closing monitoring and arch seat stability simulation; the monitoring items corresponding to the monitoring sub-item of dam body overall stability monitoring include dam body deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam body deformation simulation; The corresponding monitoring points for strain monitoring, autogenous volume deformation monitoring and stress simulation are concrete strain gauge, no-stress gauge and stress simulation results respectively; The corresponding monitoring points for anti-stereoscopic deformation monitoring, dam abutment rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam body bedrock surface opening and closing monitoring, and arch seat stability simulation are respectively the vertical line of the resistance body, the multi-point displacement meter of the arch end rock mass, the multi-point displacement meter of the riverbed foundation, the foundation surface crack meter, and the arch seat stability review result; The corresponding monitoring points for dam deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam deformation simulation are the dam vertical line, dam foundation pressure tube piezometer, transverse joint piezometer and dam deformation simulation results respectively.

[0013] In the preferred scheme, the arch dam structure safety monitoring and warning level standards are: the warning score of the monitoring object corresponding to the normal level does not exceed 20, the warning score of the monitoring object corresponding to the basically normal level is greater than 20 and does not exceed 40, the warning score of the monitoring object corresponding to the second-level warning level is greater than 40 and does not exceed 60, and the warning score of the monitoring object corresponding to the first-level warning level is greater than 60.

[0014] The present invention also provides a system for monitoring the safety of an arch dam structure, which is used to execute the above-mentioned method for monitoring the safety of an arch dam structure, comprising: The data acquisition module is used to obtain monitoring data related to arch dam stress monitoring, arch abutment stability monitoring, and overall dam body stability monitoring, and compare them with the corresponding warning values. If all monitoring data are less than the corresponding warning values, it is recorded as normal; otherwise, the warning score of the abnormal measuring point is obtained and sent to the step-by-step judgment module to execute the preset judgment process. The step-by-step judgment module is used to execute the preset judgment process.

[0015] The present invention provides a method and system for monitoring the safety of an arch dam structure, which has the following beneficial effects: 1. The first step, through identification, groups abnormal measurement points that are close in distance, of the same type, and within the same dam section into a warning measurement point group. This addresses the problem of traditional methods focusing on a single measurement point and ignoring the overall stress characteristics of the arch dam. Correlation analysis allows for more accurate identification of arch dam stress concentration risks, avoiding missed or misjudgment.

[0016] 2. In the second judgment step, the five-dimensional vector and Manhattan distance are introduced to calculate the similarity between the measurement point groups, quantify the multi-dimensional correlation relationship, and make the warning score more consistent with the spatial structural characteristics of the joint bearing of the arch dam.

[0017] 3. A four-tiered architecture, consisting of "monitoring object → monitoring sub-item → monitoring project → monitoring measurement point," was established. Arch dam safety is divided into three monitoring sub-items: stress, arch abutment stability, and overall dam stability. Each sub-item includes specific monitoring items, enabling a step-by-step risk assessment from micro-observation points to the macrostructure. The warning score for each monitoring item is the maximum value across the measurement point group to ensure that critical anomalies are not weakened. Multi-item weighting coefficients are introduced into monitoring sub-items, automatically increasing the risk level when multiple items are simultaneously abnormal, in line with the multi-physics coupling failure mechanism of arch dams.

[0018] 4. By comprehensively considering monitoring data from key arch dam locations, as well as the spatial correlation and anomaly levels between these data, the system can more accurately assess the health of the arch dam structure and promptly identify and warn of potential safety risks. Furthermore, by assigning warning scores at each level, the warning results can be broken down into specific monitoring objects and projects, providing more detailed and specific guidance for arch dam safety management and helping to improve its overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 Flowchart of the present invention; Figure 2 This is a hierarchical diagram of the arch dam structure safety monitoring index system; Figure 3 This is a schematic diagram of the results of judging the spatial secondary association relationship. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] Example 1: For dams entering their operational phase after completion, the key issue shifts from design and construction safety to operational safety. The technical challenge facing these dams is how to leverage multi-source information to reflect the dam's operational safety status. Dams typically incorporate a variety of monitoring instruments for safety monitoring. This involves compiling and analyzing information collected by various sensors located on or within the dam's surface to assess its operational performance.

[0022] Due to the structural characteristics of an arch dam, it is a holistic spatial structure and can be considered a system composed of arch beams. Horizontally, it can be divided into multiple arch rings, each supported by a rock base at both ends; vertically, it can be divided into multiple vertical cantilever beams, each supported by the dam foundation at its lower end. The dam body is spatially jointly loaded. However, the monitoring points of an arch dam are distributed in a point-like pattern, and each monitoring point can only reflect the performance information at the monitoring location. Current dam structural safety monitoring methods mostly focus on the analysis of a single monitoring point or simple statistical analysis of multiple monitoring points. They do not adequately consider the spatial correlation between different types of monitoring points at different locations, making it difficult to reflect the characteristics of the joint load-bearing of various parts of a holistic spatial structure like an arch dam. Consequently, the accuracy of monitoring and early warning results needs to be improved.

[0023] In addition, existing evaluation index systems generally do not distinguish between dam types. For different types of dams (such as gravity dams, arch dams, and earth-rock dams), the index system is divided according to the monitoring type (such as deformation / stress / seepage). The monitoring points are not linked to the working performance of the arch dam. Once an abnormality occurs at a monitoring point, it is difficult to determine the extent of the impact on the working performance of the dam.

[0024] The present invention provides a method for monitoring the safety of an arch dam structure. Figure 1 As shown, the following steps are included: The monitoring data related to the arch dam stress monitoring, arch seat stability monitoring and dam body overall stability monitoring are obtained respectively and compared with the corresponding warning values. If all the monitoring data are less than the corresponding warning values, it is recorded as normal; otherwise, the warning score of the abnormal measuring point is obtained, and the preset judgment process is executed to obtain the warning score of the monitoring items, monitoring sub-items and monitoring objects, and then an early warning is issued according to the arch dam structure safety monitoring early warning level standard.

[0025] The arch dam structure safety monitoring and early warning level standards are as follows: the early warning score of the monitored object corresponding to the normal level shall not exceed 20, the early warning score of the monitored object corresponding to the basically normal level shall be greater than 20 and not more than 40, the early warning score of the monitored object corresponding to the second-level early warning level shall be greater than 40 and not more than 60, and the early warning score of the monitored object corresponding to the first-level early warning level shall be greater than 60.

[0026] The early warning score of a monitored object is a key indicator for quantitatively assessing the safety status of an arch dam structure. By setting different early warning levels and corresponding early warning score ranges for each monitored object, real-time monitoring and early warning of the arch dam structure's safety status can be achieved. When the early warning score of a monitored object reaches or exceeds a certain threshold, the system automatically triggers the corresponding early warning mechanism, alerting relevant personnel to take timely intervention measures. Furthermore, the early warning score of the corresponding monitored object can be used for historical analysis and trend prediction of the arch dam structure's safety status. By analyzing historical data, we can understand the changing patterns of the arch dam structure's safety status over time, providing a useful reference for future safety monitoring and early warning.

[0027] At the normal level, the arch dam structure is safe and stable, and no special early warning measures are required. At the basic normal level, although the arch dam structure is still relatively safe, some minor anomalies or fluctuations have occurred, requiring close attention and timely preventive measures. At the second warning level, the safety status of the arch dam structure has shown relatively obvious anomalies, and immediate intervention measures are required to prevent further deterioration. At the first warning level, the safety status of the arch dam structure is already in an extremely dangerous state, requiring the immediate activation of the emergency plan, the organization of emergency rescue forces, and the full implementation of emergency rescue work to ensure the safety of people and property.

[0028] The preset judgment process includes the following steps: First judgment step: Check the measuring points with similar spatial distance, the same type and the same dam section. If there are any abnormalities at the same time, they will form an early warning measuring point group and enter the second judgment step; The second judgment step: calculate the similarity between the warning measurement point groups, determine the correlation weight coefficient based on the similarity calculation result, and then enter the warning score calculation step; Warning score calculation step: determining the warning score of the warning measurement point group according to the abnormality rate weight coefficient, the correlation weight coefficient and the warning score of the abnormal measurement point; the abnormality rate weight coefficient is determined according to the number of measurement points in the warning measurement point group; Step-by-step determination steps: The warning scores of all warning measurement point groups are calculated through the warning score calculation step. According to the preset arch dam structure safety monitoring indicator system and the warning scores of the warning measurement point groups, the warning scores of the monitoring items, monitoring sub-items and monitoring objects are determined step by step.

[0029] In the above embodiment, monitoring data related to arch dam stress monitoring, arch abutment stability monitoring, and overall dam body stability monitoring must first be acquired. This monitoring data is collected in real time by various sensors deployed on the surface or within the dam body. Next, the acquired monitoring data is compared with preset warning values. The warning values are determined based on a comprehensive analysis of the arch dam's design parameters, historical operating data, and safety assessment criteria. If all monitoring data are less than the corresponding warning values, the arch dam structure is considered to be in a normal state. If any monitoring data exceeds the warning values, it is considered an anomaly, and a warning score is obtained for the abnormal measuring point. These warning scores reflect the severity of the abnormal measuring point. However, the arch dam structure is a holistic spatial structure, and the warning score of a single abnormal measuring point cannot directly reflect the overall condition of the arch dam structure. Therefore, a preset judgment process is then executed to more accurately assess the health of the arch dam structure by comprehensively considering the monitoring data from various key parts of the arch dam, as well as the spatial correlation and degree of abnormality between these data.

[0030] During the pre-set judgment process, the first judgment step combines measuring points of similar spatial distance, type, and dam section to form a warning measuring point group. This step effectively captures the potential connections between different measuring points, allowing the monitoring results to better reflect the overall health of the arch dam structure and addressing the issue of insufficient consideration of spatial correlation in existing technologies. Next, considering that external loads such as water level and temperature can affect a large area of the dam, measuring points with less obvious spatial correlations or that are relatively far apart may also exhibit similar data variation patterns. Such similarity is also important for monitoring and early warning of dam structural safety. Furthermore, the monitoring data for different physical quantities of a dam are discrete time series. Therefore, in the second judgment step, the similarity between the warning measuring point groups is calculated to determine the correlation weight coefficient. The inclusion of this coefficient allows the subsequent calculation of the warning score to more accurately reflect the degree of mutual influence between different measuring points, thereby improving the accuracy and reliability of the warning. Next, in the warning score calculation step, the abnormality rate weight coefficient is determined based on the number of measuring points in the warning measuring point group. The application of this anomaly rate weighting factor ensures that the warning score reflects the number and distribution of abnormal measurement points, further enhancing the representativeness of the warning results. Finally, based on the pre-defined arch dam structural safety monitoring indicator system and the warning scores for the warning measurement point groups, warning scores are determined for each monitoring item, sub-item, and object. This step refines the warning results to specific monitoring objects and items, providing a more detailed and specific reference for arch dam safety management.

[0031] By comprehensively considering monitoring data from key arch dam locations, as well as the spatial correlation and anomaly levels between these data, the health of the arch dam structure can be more accurately assessed, allowing for the timely detection and warning of potential safety risks. Furthermore, by assigning warning scores at each level, the warning results can be broken down into specific monitoring objects and projects, providing more detailed and specific guidance for arch dam safety management and helping to improve its overall performance.

[0032] The following is a detailed description of the preset judgment process: S1. First judgment step: Check the measuring points with similar spatial distance, the same type and the same dam section. If there are simultaneous abnormalities, form an early warning measuring point group and enter the second judgment step.

[0033] S2. Second judgment step: Calculate the similarity between the warning measurement point groups, determine the correlation weight coefficient based on the similarity calculation result, and then enter the warning score calculation step.

[0034] Calculate the similarity between warning measurement point groups and determine the association weight coefficient based on the similarity calculation results, including: 1) For any two measuring points between the early warning measuring point groups, a five-dimensional vector including spatial coordinates, time and monitoring data is compiled.

[0035] For any two points in the early warning point group, a vector consisting of five dimensions is compiled. These five dimensions are: spatial coordinates: including longitude, latitude, and altitude, which can be simplified to two-dimensional or three-dimensional coordinates depending on the specific application scenario; time: representing the moment of data collection; and monitoring data: the specific monitoring value of the point at a certain moment.

[0036] The specific implementation is as follows: For a measuring point A in a certain early warning measuring point group and a measuring point B in another early warning measuring point group, the five-dimensional vectors including spatial coordinates, time and monitoring data are compiled as follows: the vector corresponding to measuring point A L =( L 1, L 2, L 3. L 4. L 5), the vector corresponding to the measuring point A M =( M 1, M 2, M 3. M 4. M 5).

[0037] in, L 1. L 2. L 3. L 4 and L 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point A; M 1. M 2. M 3. M 4 and M 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point B.

[0038] The five-dimensional vector comprehensively considers multiple dimensions, including space, time, and monitoring data, making data processing more comprehensive and accurate. By compiling the five-dimensional vector and performing subsequent processing, more potential correlations can be identified, thereby improving the robustness and reliability of the early warning system used for arch dam structural safety monitoring.

[0039] 2) Since the dimensions of a five-dimensional vector may have different dimensions and numerical ranges, these vectors need to be normalized for fair comparison. The purpose of normalization is to convert the values of each dimension to the same scale, for example, the interval [0, 1] or [-1, 1].

[0040] After normalizing the five-dimensional vector, the corresponding similarity distance is calculated using the similarity distance calculation formula, and then the Manhattan distance is calculated. The comparison relationship between the Manhattan distance and the control threshold is used to determine the multidimensional correlation relationship between the early warning measurement point groups, thereby determining the correlation weight coefficient.

[0041] A control threshold is set to determine whether the Manhattan distance between two measurement points is sufficiently small to determine whether they are in a multidimensional relationship. If the Manhattan distance between two measurement points is less than or equal to the control threshold, a multidimensional relationship is considered to exist between them. Based on whether a multidimensional relationship exists between the two measurement points, a correlation weight coefficient is assigned to them. This correlation weight coefficient is then retrieved and used in the subsequent warning score calculation step.

[0042] By comprehensively considering multiple dimensions to calculate the similarities between warning point groups and determining the association weight coefficients based on these similarities, not only the accuracy and reliability of the warning system are improved, but also its flexibility and adaptability are enhanced.

[0043] The normalization operation for the five-dimensional vector is as follows: Using calculation formula and For vector L and vector M Perform normalization processing, where is a vector L No. i parameters, is a vector M No. j parameters, is a vector L No. i The normalized value of the parameters, is a vector M No. j The normalized value of the parameters, 、 are vectors L No. i The maximum and minimum values of the parameters, 、 are vectors M No. j The maximum and minimum values of the parameters.

[0044] The purpose of normalizing the five-dimensional vector is to transform the vector L and vector MEach parameter in is converted to the same numerical range, which can be between 0 and 1 for example. When the similarity calculation is performed on the processed data, the deviation caused by different dimensions can be avoided, thereby improving the accuracy of the analysis. That is, the calculation formula in the above implementation method can eliminate the vector L and vector M The dimensional differences between different dimensional data make the individual dimensional data comparable in value, thereby improving the accuracy of subsequent analysis.

[0045] The similarity distance calculation formula is used to calculate the corresponding similarity distance, and the comparison relationship between the similarity distance and the control threshold is used to determine the multi-dimensional correlation relationship between the early warning measurement point groups, thereby determining the correlation weight coefficient. The specific operation is as follows: Using calculation formula Calculate the corresponding similarity distance value , for similarity distance values After normalization, the monitoring data time series of measuring points A and B are iterated step by step: ,in, k is the number of time series monitoring data, D It is the comprehensive Manhattan distance of the time series monitoring data of measuring point A and measuring point B; it defines the unit Manhattan distance of two time series monitoring data. for , then if the unit Manhattan distance of two time series monitoring data is If the control threshold value is within the range, it means that there is a multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.02; otherwise, it means that there is no multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.0.

[0046] In this embodiment, the control threshold is 0≤DISMD≤0.25, that is, if the unit Manhattan distance of two time series monitoring data is If it is greater than or equal to 0 and less than or equal to 0.25, the value of the association weight coefficient is determined to be 1.02; otherwise, the value of the association weight coefficient is determined to be 1.0.

[0047] S3, early warning score calculation step: according to the abnormality rate weight coefficient, the correlation weight coefficient and the early warning score of the abnormal measurement point, the early warning score of the early warning measurement point group is determined; the abnormality rate weight coefficient is determined according to the number of measurement points in the early warning measurement point group. The specific operation is as follows: The warning score S of the warning measurement point group is calculated using the formula S=d×R×F; Where d is the abnormality rate weight coefficient. If the number of measuring points in the early warning measuring point group is 1, the value is 1.0; otherwise, the value is 1.02. R is the association weight coefficient. If there is no multidimensional association relationship between the early warning measuring point groups, the value is 1.0; otherwise, the value is 1.02. F is the warning score of the abnormal measuring point. The normal score is 0, the first-level warning score is 20, and the second-level warning score is 50.

[0048] The value of the anomaly rate weight coefficient d reflects the increased emphasis on anomalies as the number of measurement points increases. The value of the correlation weight coefficient R reflects the increased emphasis on warnings when correlations exist between measurement points in different warning point groups. The warning score S for each warning point group is calculated using the formula S = d × R × F. This score reflects the overall warning status of the warning point group, including factors such as the anomaly rate, correlation, and degree of anomaly.

[0049] S4. Step-by-step determination step: The warning scores of all warning measurement point groups are calculated through the warning score calculation step. According to the preset arch dam structure safety monitoring indicator system and the warning scores of the warning measurement point groups, the warning scores of the monitoring items, monitoring sub-items and monitoring objects are determined step by step.

[0050] The preset arch dam structure safety monitoring indicator system is divided into monitoring objects, monitoring sub-items, monitoring projects and monitoring measurement points from top to bottom.

[0051] Among them, the warning score of the monitoring project is equal to the maximum warning score of the warning measurement point group; the warning score of the monitoring sub-item is determined by the maximum warning score of the monitoring items contained in the monitoring sub-item. Among them, if there are multiple monitoring items with warning scores greater than 0, the multi-item weight coefficient is 1.2, otherwise it is 1.0; the warning score of the monitoring object is determined based on the sum of the warning scores of all monitoring sub-items.

[0052] The monitoring object represents the entire arch dam structure or its major components, representing the highest level of monitoring. Monitoring sub-items further subdivide the monitoring object, representing different parts or functional areas within the monitoring object. Monitoring projects further subdivide the monitoring sub-items, representing specific monitoring content or indicators. Monitoring measurement points, located at the lowest level of the monitoring system, are the points where data collection and monitoring are actually performed.

[0053] The warning score for a monitoring item is directly equal to the maximum warning score of the included warning measurement point groups. This means that if even one measurement point has a higher warning score, the warning score for the entire monitoring item will be increased accordingly. The warning score for a monitoring sub-item is determined based on the warning scores of the included monitoring items. If multiple monitoring items have warning scores greater than 0 (i.e., anomalies), the multi-item weighting factor is set to 1.2 to reflect the more serious situation when multiple items have anomalies simultaneously. The warning score for a monitoring sub-item is calculated as the maximum warning score of the included monitoring items multiplied by the multi-item weighting factor (if applicable). If only one item has a warning score greater than 0, the warning score of that item is used directly. The warning score for a monitoring object is determined based on the warning scores of all included monitoring sub-items. Specifically, the warning score for a monitoring object is the sum of the warning scores of all its monitoring sub-items. This reflects the overall security status of the monitored object and is a comprehensive reflection of the security status of all sub-items.

[0054] The aforementioned method of determining early warning scores at each level allows for a more detailed and comprehensive assessment of the arch dam structure's safety status. Different levels of early warning scores not only reflect the arch dam's safety status at different levels but also provide a strong basis for subsequent early warning responses and decision support. For example, if a monitoring item receives a high early warning score, immediate attention can be paid to the specific situation and targeted intervention measures can be taken. If a monitoring item or object receives a high early warning score, a higher-level early warning response mechanism may be necessary to ensure the safe and stable operation of the arch dam structure.

[0055] In this embodiment, if Figure 2 As shown, the monitoring object is the target arch dam structure; the monitoring sub-items include arch dam stress monitoring, arch seat stability monitoring and dam body overall stability monitoring.

[0056] Among them, the monitoring items corresponding to the arch dam stress monitoring sub-item include strain monitoring, autogenous volume deformation monitoring and stress simulation.

[0057] The monitoring items corresponding to the arch seat stability monitoring sub-item include anti-three-dimensional deformation monitoring, dam shoulder rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam bed rock surface opening and closing monitoring and arch seat stability simulation.

[0058] The monitoring items corresponding to the sub-item of dam body overall stability monitoring include dam body deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam body deformation simulation.

[0059] The corresponding monitoring points for strain monitoring, spontaneous volume deformation monitoring, and stress simulation are concrete strain gauges, non-strain gauges, and stress simulation results, respectively. Strain monitoring projects correspond to concrete strain gauge monitoring points, spontaneous volume deformation monitoring projects correspond to non-strain gauge monitoring points, and so on. There are more than one monitoring point; for example, concrete strain gauge monitoring points may actually have dozens. Furthermore, the indicator system is divided from top to bottom, but the warning scores are obtained from the bottom up.

[0060] Taking the arch dam stress monitoring monitoring sub-item as an example, if the warning scores of the three monitoring items of strain monitoring, spontaneous volume deformation monitoring and stress simulation are determined, the warning score of the arch dam stress monitoring sub-item depends on the maximum value of the three. If there are multiple monitoring items with warning scores greater than 0, the multi-item weight coefficient is taken as 1.2; otherwise, it is taken as 1.0.

[0061] The corresponding monitoring points for anti-three-dimensional deformation monitoring, dam shoulder rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam bedrock surface opening and closing monitoring and arch seat stability simulation are the vertical line of the resistance body, the multi-point displacement meter of the arch end rock mass, the multi-point displacement meter of the riverbed foundation, the foundation surface crack meter and the arch seat stability review result.

[0062] The corresponding monitoring points for dam deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam deformation simulation are the dam vertical line, dam foundation pressure tube piezometer, transverse joint piezometer and dam deformation simulation results respectively.

[0063] The monitoring target is the target arch dam structure, representing the entire structure requiring comprehensive monitoring and safety assessment. Sub-items are further subdivided into monitoring areas to more accurately assess the safety of the arch dam structure in various aspects. These include: 1) Arch dam stress monitoring: focusing on the safety performance of the arch dam under stress; 2) Abutment stability monitoring: assessing the stability and safety of the arch dam foundation, i.e., the abutment; and 3) Dam body overall stability monitoring: comprehensively monitoring the overall deformation and stability of the dam body.

[0064] Specifically, arch dam stress monitoring includes: 1) strain monitoring: using concrete strain gauges to measure the strain of concrete materials to evaluate the stress state of the arch dam; 2) autogenous volume deformation monitoring: using stress-free gauges to monitor the volume changes of concrete in the absence of external forces to reflect the autogenous deformation of concrete; 3) stress simulation: using simulation software or models to perform stress analysis and obtain stress simulation results to predict and evaluate the stress distribution and state of the arch dam.

[0065] Arch seat stability monitoring includes: 1) Resistance body deformation monitoring: Use the resistance body vertical line to monitor the deformation of the arch seat resistance body and evaluate its stability; 2) Dam shoulder rock deformation monitoring: Use the arch end rock multi-point displacement meter to measure the deformation of the dam shoulder rock to determine whether it is stable; 3) Riverbed dam section foundation deformation monitoring: Use the riverbed foundation multi-point displacement meter to monitor the deformation of the riverbed dam section foundation and evaluate its impact on the stability of the arch dam; 4) Dam body bedrock surface opening and closing monitoring: Use the foundation surface joint meter to monitor the opening and closing of the dam body and the bedrock surface to determine the stability of the bedrock surface; 5) Arch seat stability simulation: Perform a simulation analysis of the arch seat stability, obtain the arch seat stability review result, and evaluate the stability performance of the arch seat.

[0066] The overall stability monitoring of the dam body includes: 1) Dam deformation monitoring: using the dam body vertical line to monitor the overall deformation of the dam body, including deformation in the vertical and horizontal directions; 2) Dam foundation seepage and pressure monitoring: monitoring the seepage and pressure inside the dam foundation through the dam foundation pressure tube piezometer to evaluate the safety of the dam foundation; 3) Transverse joint opening and closing monitoring: using the transverse joint seam gauge to measure the opening and closing of the dam body transverse joints to determine the deformation and stability inside the dam body; 4) Dam deformation simulation: conducting simulation analysis of the dam body deformation to obtain the dam body deformation simulation results to predict and evaluate the overall deformation and stability of the dam body.

[0067] Example 2: According to the method disclosed in Example 1, the entire arch dam structure is safety monitored. The specific steps are as follows: Step 1: Obtain monitoring data related to arch dam stress monitoring, arch seat stability monitoring, and dam body overall stability monitoring, and compare them with the corresponding warning values; if all types of monitoring data are less than the warning values, the evaluation conclusion is normal; if any monitoring exceeds the warning value, that is, a warning measuring point appears, proceed to the next step.

[0068] Step 2: Spatial first-level correlation determination: This refers to the correlation determination of closely spaced measuring points, specifically measuring points of the same type and dam section. First, determine whether any of the measuring points in the spatial one-dimensional correlation relationship have simultaneous abnormalities. If so, they are included in the early warning measuring point group. Each measuring point of each type and dam section is considered a group of early warning measuring points. If not, proceed to step 4.

[0069] Step 3: Determine Spatial Secondary Correlation: External loads such as water level and temperature may affect a large area of the dam. Measuring points with less obvious spatial correlations or that are far apart may also exhibit similar data variation patterns. This similarity is also important for monitoring and early warning of dam structural safety. The monitoring data for different physical quantities of the dam are discrete time series. This application considers the potential for correlations between two time series across multiple dimensions, including spatial, temporal, and numerical values.

[0070] For measurement points between multiple early warning measurement point groups, the spatial secondary judgment method is as follows: For any two measuring points A and B, a 5-dimensional vector including spatial coordinates, time, and monitoring data is compiled: the vector corresponding to measuring point A L =( L 1, L 2, L 3. L 4. L 5), the vector corresponding to the measuring point A M =( M 1, M 2, M 3. M 4. M 5); Among them, L 1. L 2. L 3. L 4 and L 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point A; M 1. M 2. M 3. M 4 and M 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point B. The above data are all normalized. Taking the monitoring data as an example, the normalization process is as follows: Using the calculation formula ,as well as Pair Vector L and vector M Perform normalization processing, where is a vector L No. i parameters, is a vector M No. j parameters, is a vector L No. i The normalized value of the parameters, is a vector M No. j The normalized value of the parameters, 、 are vectors L No. i The maximum and minimum values of the parameters, 、 are vectors M No. j The maximum and minimum values of the parameters.

[0071] Manhattan distance (MD) can be used to reflect the multi-dimensional comprehensive distance between two monitoring data series, which can be expressed as: ,right After normalization, the data sequences of measuring points A and B are iterated step by step: , where k is the number of measurement points, D It is the comprehensive Manhattan distance of the time series monitoring data of measuring point A and measuring point B; it defines the unit Manhattan distance of two time series monitoring data. for , due to the Manhattan distance of each monitoring data Normalized, so is a constant between 0 and 1. The control threshold for the similarity between the data sequences of measurement points A and B is defined as 0 ≤ DISMD ≤ 0.25. If a data similarity exists, the warning scores of the related warning measurement point groups are weighted by the correlation weight coefficient. If there is no data similarity, the correlation weight coefficient is 1.0, and if there is a data similarity, the correlation weight coefficient is 1.02.

[0072] Step 4: Calculate the warning score S for the warning point group using the formula: S = d × R × F. Where d is the abnormality rate weight coefficient, which is 1.0 if there is only one point in the warning point group and 1.02 otherwise. R is the association weight coefficient, which is 1.0 if there is no multidimensional association between warning point groups and 1.02 otherwise. F is the warning score for abnormal points, with a normal score of 0, a first-level warning score of 20, and a second-level warning score of 50.

[0073] Step 5: Repeat steps 2 to 4 to obtain the warning scores of all warning measurement point groups.

[0074] Step 6: The warning score of the monitoring project is equal to the maximum warning score of the warning measurement point group.

[0075] Step 7: The warning score of the monitoring sub-item depends on the maximum warning score of the monitoring items contained in the monitoring sub-item. If there are multiple monitoring items with warning scores greater than 0, the multi-item weight coefficient is taken as 1.2; otherwise, it is taken as 1.0.

[0076] Step 8: The warning score of the monitored object depends on the sum of the warning scores of the monitored sub-items: ; Where, is the early warning score of the safety status of the monitored object, i.e. the arch dam structure. q i For the i The warning score of each monitoring item.

[0077] Arch dam structure safety status warning The arch dam structure safety status warning level is divided into 3 levels, see Table 1 below for details.

[0078]

[0079] Taking the deformation of a certain arch dam as the monitoring point input, the measuring point entering the safety monitoring has determined the monitoring warning status through the measuring point validity abnormality evaluation link, and only the single measuring point evaluation result is used as the input condition.

[0080] At the same time (January 3, 2024), the three vertical lines of the dam body simultaneously issued a level 1 warning with a warning score of 20, as shown in Table 2.

[0081]

[0082] The spatial first-level association obtained three warning measurement point groups, as shown in Table 1. The spatial second-level association judgment results are shown in Table 3. There is a group of measurement points (PL03DB04 and PL04DB08) with a normalized data Manhattan distance of 0.20, which is less than the threshold of 0.25, indicating that the two have a spatial second-level association relationship, as shown in Table 3. Figure 3 The warning score of the early warning measuring point group is 20.4, 20.4 and 20 respectively. The early warning score of the monitored object is 20.4, and the early warning level of the arch dam structure safety condition is normal.

[0083]

[0084] Example 3: This embodiment provides a system for monitoring the safety of an arch dam structure, which is used in the method for monitoring the safety of an arch dam structure provided in the above embodiment, including: The data acquisition module is used to obtain monitoring data related to arch dam stress monitoring, arch abutment stability monitoring, and overall dam body stability monitoring, and compare them with the corresponding warning values. If all monitoring data are less than the corresponding warning values, it is recorded as normal; otherwise, the warning score of the abnormal measuring point is obtained and sent to the step-by-step judgment module to execute the preset judgment process. The step-by-step judgment module is used to execute a preset judgment process, which includes: a first judgment step: checking measuring points of similar spatial distance, same type and same dam section. If there are simultaneous abnormalities, they are formed into an early warning measuring point group and the second judgment step is entered; otherwise, the early warning score calculation step is entered; a second judgment step: calculating the similarity between the early warning measuring point groups to determine the association weight coefficient based on the calculation result; an early warning score calculation step: determining the early warning score of the early warning measuring point group based on the abnormality rate weight coefficient, the association weight coefficient and the early warning score of the abnormal measuring point; the abnormality rate weight coefficient is determined based on the number of measuring points in the early warning measuring point group; a step-by-step determination step: determining the early warning scores of the monitoring items, monitoring sub-items and monitoring objects based on the preset arch dam structure safety monitoring indicator system and the early warning scores of the early warning measuring point groups.

[0085] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring the safety of an arch dam structure, characterized in that: The following steps are involved: Obtain monitoring data related to arch dam stress monitoring, arch seat stability monitoring, and dam body overall stability monitoring, and compare them with the corresponding warning values. If all monitoring data are less than the corresponding warning values, it is recorded as normal; Otherwise, obtain the warning score of the abnormal measuring point and execute the preset judgment process to obtain the warning score of the monitoring item, monitoring sub-item and monitoring object, and then issue an early warning according to the arch dam structure safety monitoring early warning level standard; The preset judgment process includes the following steps: First judgment step: Check the measuring points with similar spatial distance, the same type and the same dam section. If there are any abnormalities at the same time, they will form an early warning measuring point group and enter the second judgment step; The second judgment step: calculate the similarity between the warning measurement point groups, determine the correlation weight coefficient based on the similarity calculation result, and then enter the warning score calculation step; Warning score calculation step: determining the warning score of the warning measurement point group according to the abnormality rate weight coefficient, the correlation weight coefficient and the warning score of the abnormal measurement point; the abnormality rate weight coefficient is determined according to the number of measurement points in the warning measurement point group; Step-by-step determination steps: The warning scores of all warning measurement point groups are calculated through the warning score calculation step. According to the preset arch dam structure safety monitoring indicator system and the warning scores of the warning measurement point groups, the warning scores of the monitoring items, monitoring sub-items and monitoring objects are determined step by step.

2. The arch dam structure safety monitoring method according to claim 1, characterized in that: The calculating of similarities between the early warning measurement point groups and determining the correlation weight coefficient according to the similarity calculation results include: For any two measuring points between the early warning measuring point groups, a five-dimensional vector including spatial coordinates, time and monitoring data is compiled; After normalizing the five-dimensional vector, the corresponding similarity distance is calculated using the similarity distance calculation formula, and then the Manhattan distance is calculated. The comparison relationship between the Manhattan distance and the control threshold is used to determine the multidimensional correlation relationship between the early warning measurement point groups, thereby determining the correlation weight coefficient.

3. The arch dam structure safety monitoring method according to claim 2, characterized in that: For any two measuring points between the early warning measuring point groups, a five-dimensional vector including spatial coordinates, time and monitoring data is compiled, including: For a measuring point A in a certain early warning measuring point group and a measuring point B in another early warning measuring point group, the five-dimensional vectors including spatial coordinates, time and monitoring data are compiled as follows: the vector corresponding to measuring point A L =( L 1, L 2, L 3. L 4. L 5), the vector corresponding to the measuring point A M =( M 1, M 2, M 3. M 4. M 5); in, L 1. L 2. L 3. L 4 and L 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point A; M 1. M 2. M 3. M 4 and M 5 are the spatial position X coordinate, Y coordinate, Z coordinate, time and monitoring data of the monitoring data time series corresponding to the measuring point B.

4. The arch dam structure safety monitoring method according to claim 3, characterized in that: Normalizing the five-dimensional vector includes: Using calculation formula and For vector L and vector M Perform normalization processing, where is a vector L No. i parameters, is a vector M No. j parameters, is a vector L No. i The normalized value of the parameters, is a vector M No. j The normalized value of the parameters, 、 are vectors L No. i The maximum and minimum values of the parameters, 、 are vectors M No. j The maximum and minimum values of the parameters.

5. The arch dam structure safety monitoring method according to claim 4, characterized in that: The method of calculating the corresponding similarity distance using the similarity distance calculation formula and determining the multi-dimensional correlation between the early warning measurement point groups using the comparison relationship between the similarity distance and the control threshold, thereby determining the correlation weight coefficient, includes: Using calculation formula Calculate the corresponding similarity distance value , for similarity distance values After normalization, the monitoring data time series of measuring points A and B are iterated step by step: ,in, k is the number of time series monitoring data, D It is the comprehensive Manhattan distance of the time series monitoring data of measuring point A and measuring point B; it defines the unit Manhattan distance of two time series monitoring data. for , then if the unit Manhattan distance of two time series monitoring data is If the control threshold value is within the range, it means that there is a multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.02; otherwise, it means that there is no multidimensional correlation relationship between the early warning measurement point groups, and the value of the correlation weight coefficient is determined to be 1.

0.

6. The arch dam structure safety monitoring method according to claim 1, characterized in that: Determining the warning score of the warning measurement point group according to the abnormality rate weight coefficient, the correlation weight coefficient and the warning score of the abnormal measurement point includes: The warning score S of the warning measurement point group is calculated using the formula S=d×R×F; Where d is the abnormal rate weight coefficient. If the number of measuring points in the early warning measuring point group is 1, the value is 1.0; otherwise, the value is 1.02; R is the association weight coefficient. If there is no multidimensional association relationship between the early warning measuring point groups, the value is 1.0; otherwise, the value is 1.02; F is the early warning score of the abnormal measuring point.

7. The arch dam structure safety monitoring method according to claim 1, characterized in that: The aforementioned step-by-step determination of the warning scores of monitoring items, monitoring sub-items and monitoring objects based on the preset arch dam structure safety monitoring indicator system and the warning scores of the warning measurement point group includes: The preset arch dam structure safety monitoring indicator system is divided into monitoring objects, monitoring sub-items, monitoring projects and monitoring measurement points from top to bottom; Among them, the warning score of the monitoring project is equal to the maximum warning score of the warning measurement point group; the warning score of the monitoring sub-item is determined by the maximum warning score of the monitoring items contained in the monitoring sub-item. Among them, if there are multiple monitoring items with warning scores greater than 0, the multi-item weight coefficient is 1.2, otherwise it is 1.0; the warning score of the monitoring object is determined based on the sum of the warning scores of all monitoring sub-items.

8. The arch dam structure safety monitoring method according to claim 7, characterized in that: The monitoring object is a target arch dam structure; the monitoring sub-items include arch dam stress monitoring, arch seat stability monitoring and dam body overall stability monitoring, wherein the monitoring items corresponding to the monitoring sub-item of arch dam stress monitoring include strain monitoring, autogenous volume deformation monitoring and stress simulation; the monitoring items corresponding to the monitoring sub-item of arch seat stability monitoring include anti-three-dimensional deformation monitoring, dam abutment rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam body bedrock surface opening and closing monitoring and arch seat stability simulation; the monitoring items corresponding to the monitoring sub-item of dam body overall stability monitoring include dam body deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam body deformation simulation; The corresponding monitoring points for strain monitoring, autogenous volume deformation monitoring and stress simulation are concrete strain gauge, no-stress gauge and stress simulation results respectively; The corresponding monitoring points for anti-stereoscopic deformation monitoring, dam abutment rock deformation monitoring, riverbed dam section foundation deformation monitoring, dam body bedrock surface opening and closing monitoring, and arch seat stability simulation are respectively the vertical line of the resistance body, the multi-point displacement meter of the arch end rock mass, the multi-point displacement meter of the riverbed foundation, the foundation surface crack meter, and the arch seat stability review result; The corresponding monitoring points for dam deformation monitoring, dam foundation seepage and pressure monitoring, transverse joint opening and closing monitoring and dam deformation simulation are the dam vertical line, dam foundation pressure tube piezometer, transverse joint piezometer and dam deformation simulation results respectively.

9. The arch dam structure safety monitoring method according to claim 1, characterized in that: The arch dam structure safety monitoring and early warning level standards are as follows: the early warning score of the monitored object corresponding to the normal level shall not exceed 20, the early warning score of the monitored object corresponding to the basically normal level shall be greater than 20 and not more than 40, the early warning score of the monitored object corresponding to the second-level early warning level shall be greater than 40 and not more than 60, and the early warning score of the monitored object corresponding to the first-level early warning level shall be greater than 60.

10. A system for monitoring the safety of an arch dam structure, characterized in that: The method for monitoring the safety of an arch dam structure according to any one of claims 1 to 9 comprises: The data acquisition module is used to obtain monitoring data related to arch dam stress monitoring, arch abutment stability monitoring, and overall dam body stability monitoring, and compare them with the corresponding warning values. If all monitoring data are less than the corresponding warning values, it is recorded as normal; otherwise, the warning score of the abnormal measuring point is obtained and sent to the step-by-step judgment module to execute the preset judgment process. The step-by-step judgment module is used to execute the preset judgment process.

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