Dam osmotic pressure monitoring and analyzing method based on sensing data

Abstract

The invention relates to the technical field of dam safety in water conservancy projects, in particular to a dam osmotic pressure monitoring and analyzing method based on sensing data. According to the method, the arrangement density of the monitoring points in the key structure area of the dam body is determined, the seepage pressure severity levels of the historical seepage pressure areas are analyzed to determine the arrangement density of the corresponding monitoring points, and the monitoring points are arranged on the surface of the dam body in combination with the boundary contours of the key structure area and the historical seepage pressure areas; initial osmotic pressure and ring influence factors of each monitoring point are collected, and environment similar points are clustered to form monitoring sub-regions; constructing an osmotic pressure correction compensation model of linear regression, and eliminating environmental influence to output an actual osmotic pressure value; and comparing the actual osmotic pressure of the monitoring points in the same monitoring sub-region to determine abnormal monitoring points, and determining and feeding back the positions of the osmotic pressure monitoring points in combination with a historical osmotic pressure time sequence trend. According to the method, the layout of monitoring points can be optimized, data errors are reduced, misjudgment and missed judgment of abnormal recognition are reduced, and the reliability of dam safety monitoring is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering safety technology, and specifically to a method for monitoring and analyzing dam seepage pressure based on sensor data. Background Technology

[0002] As the core structure of water conservancy projects, the seepage pressure state of dams directly reflects the seepage prevention performance and structural stability of the dam body, and is a key indicator for assessing dam safety. With the intelligent development of water conservancy projects, traditional seepage pressure monitoring methods have gradually revealed the following technical defects: existing methods mostly rely on manual experience or adopt a uniform layout, only setting up monitoring points according to a uniform grid spacing or a single structural dimension, without combining the key structural attributes of the dam body and the historical seepage pressure risk distribution. This results in sparse monitoring points in high-risk areas and over-monitoring in low-risk areas, ultimately failing to guarantee the monitoring accuracy in high-risk areas and causing a waste of resources in sensor procurement, installation, and subsequent operation and maintenance.

[0003] Existing methods do not consider the regional differences in environmental impact factors. They often directly use the original environmental impact factor values ​​of the entire monitoring area for analysis and use the collected initial seepage pressure values ​​directly as the actual seepage pressure values. This fails to eliminate the influence of environmental interference on seepage pressure measurement, resulting in a large deviation between the calculated actual seepage pressure value and the actual seepage pressure state of the dam body, and thus cannot provide an accurate data basis for safety assessment.

[0004] Existing methods rely on a single logic for anomaly identification, judging anomalies solely by comparing the current seepage pressure value with a fixed threshold. They fail to consider the historical seepage pressure trends at monitoring points, leading to misjudgments of short-term normal fluctuations as anomalies and omissions of long-term, slowly increasing potential hazards. This results in the inability to accurately locate long-term potential hazards and delays in hazard mitigation. Summary of the Invention

[0005] The purpose of this invention is to realize the monitoring and analysis of dam seepage pressure, and to provide a method for monitoring and analyzing dam seepage pressure based on sensor data, thereby improving the intelligence and accuracy of dam safety monitoring.

[0006] The technical solution adopted by the present invention to solve its technical problem is: to provide a method for monitoring and analyzing dam seepage pressure based on sensor data, including: acquiring key structural areas on the surface of the dam body, dividing them into grids and determining the density of monitoring points in the key structural areas.

[0007] Historical data of each historical seepage pressure area was extracted from the historical seepage pressure dataset of the dam. The severity of seepage pressure in each historical seepage pressure area was analyzed, the density of monitoring points in each historical seepage pressure area was determined, and monitoring points were deployed on the surface of the dam body according to the boundary contours of the key structural areas and each historical seepage pressure area.

[0008] Initial seepage pressure values ​​and environmental impact factors were collected at each monitoring point on the dam surface. The similarity of environmental impact factors between each monitoring point and all adjacent monitoring points within its set range was analyzed. Monitoring points with similarity greater than the set similarity threshold were clustered to form monitoring sub-regions.

[0009] A pressure correction and compensation model is constructed. The initial pressure values ​​of all monitoring points in each monitoring sub-region and the environmental influencing factors are substituted into the pressure correction and compensation model, and the actual pressure values ​​of each monitoring point are output.

[0010] The actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region are compared to identify abnormal monitoring points. The location of the seepage pressure monitoring points is determined by combining the historical seepage pressure value time series of the abnormal monitoring points, and feedback is provided.

[0011] The present invention has the following beneficial effects: (1) The present invention divides the key structural area of ​​the dam body into grids and combines the boundary contours of the key structural area and the historical seepage pressure area to optimize the layout of the monitoring point density. This ensures the basic monitoring needs of the key structural area and strengthens the monitoring intensity of the historical high-risk area, avoids the waste of sensor resources, eliminates monitoring blind spots, and improves the rationality of monitoring coverage.

[0012] (2) This invention takes into account the regional differences of environmental impact factors, standardizes the environmental impact factors, and clusters each monitoring point according to the similarity of environmental impact factors to form each monitoring sub-region. The initial seepage pressure value of all monitoring points in each monitoring sub-region and the environmental impact factors are substituted into the seepage pressure correction and compensation model to output the actual seepage pressure value of each monitoring point. This can eliminate the interference of environmental impact factors on the actual seepage pressure value, so that the data can accurately reflect the seepage pressure state of the dam itself and provide an accurate data basis for anomaly identification.

[0013] (3) This invention determines abnormal monitoring points by coordinating the determination of the actual seepage pressure values ​​of monitoring points in the same monitoring sub-region, and determines the location of abnormal monitoring points by combining the historical seepage pressure value time sequence of abnormal monitoring points and providing feedback. This avoids misjudging short-term normal fluctuations as abnormal and missing potential hidden dangers that grow slowly over a long period of time, significantly reducing the misjudgment rate and missed judgment rate of anomaly identification, and ensuring the accuracy and reliability of dam safety monitoring. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1This is a flowchart illustrating a dam seepage pressure monitoring and analysis method based on sensor data provided by the present invention.

[0016] Figure 2 This is the process of clustering monitoring points based on similarity to form monitoring sub-regions in this invention.

[0017] Figure 3 This is the process for determining the seepage pressure monitoring point in this invention. Detailed Implementation

[0018] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0019] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0020] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0021] The following description, in conjunction with the accompanying drawings, details a specific scheme for a dam seepage pressure monitoring and analysis method based on sensor data provided by the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a method for monitoring and analyzing dam seepage pressure based on sensor data, including: acquiring key structural areas on the surface of the dam body, dividing them into grids and determining the density of monitoring points in the key structural areas.

[0023] In a preferred embodiment of the present invention, the step of dividing the key structural area into several monitoring points according to a grid includes: obtaining each key structural area and its corresponding structural function classification on the surface of the dam body according to the dam body structure layout diagram; obtaining the dam's three-dimensional point cloud data by performing a three-dimensional scan of the dam body surface; extracting the boundary contour coordinates of each key structural area to form a closed regional vector graphic; presetting the corresponding grid unit spacing according to the structural function classification of each key structural area; dividing the regional vector graphic of each key structural area into a grid; and using the geometric center of each grid unit as the location for the monitoring point.

[0024] The density of monitoring points in key structural areas is determined by calculating the ratio of the number of monitoring points in each key structural area to the area of ​​the key structural area.

[0025] It should be noted that, based on the dam body cross-section and plan layout diagrams in the dam body structural layout plan, the functional priorities of the dam body structure are defined, and the critical structural areas are classified. The structural function classification includes high priority, medium priority, and low priority. High-priority critical structural areas refer to areas that directly bear the weight of the dam body and are prone to abnormal seepage pressure, such as the dam foundation, cutoff wall, and dam gallery. Medium-priority critical structural areas refer to areas that are greatly affected by water level fluctuations and whose seepage pressure easily fluctuates with environmental changes, such as the water level fluctuation zone in the middle of the dam body. Low-priority critical structural areas refer to areas with low seepage pressure risk, mainly affected by monitoring environmental interference rather than structural hazards.

[0026] Different structural functions require different seepage pressure risks and monitoring point density. Pre-setting corresponding grid unit spacing for each key structural area based on its structural function classification according to implementation specifications ensures that the spacing meets accuracy requirements while avoiding resource waste due to excessive deployment. In this embodiment, based on the requirements for seepage pressure monitoring point density in the dam safety monitoring technical specifications, the grid unit spacing for high priority is set to 2 meters, for medium priority to 4 meters, and for low priority to 8 meters.

[0027] By dividing the key structural area into grids and calculating the ratio of the number of monitoring points in each key structural area to the area of ​​the key structural area, the density of monitoring points is converted into a directly comparable value and the density of monitoring points in the key structural area is determined.

[0028] Historical data of each historical seepage pressure area was extracted from the historical seepage pressure dataset of the dam body. The severity of seepage pressure in each historical seepage pressure area was analyzed, the density of monitoring points in each historical seepage pressure area was determined, and monitoring points were deployed on the surface of the dam body according to the boundary contours of the key structural areas and each historical seepage pressure area.

[0029] The determination of the monitoring point deployment density for each historical seepage pressure area includes: matching the historical seepage pressure values ​​in the historical data of each historical seepage pressure area with the seepage pressure value range corresponding to each set seepage pressure severity level, obtaining the seepage pressure severity level corresponding to each historical seepage pressure value, calculating the percentage of each seepage pressure severity level in each historical seepage pressure area, and taking the seepage pressure severity level with the highest percentage as the seepage pressure severity level of each historical seepage pressure area.

[0030] The monitoring point density for each historical seepage pressure area is determined by matching the preset monitoring point density with the corresponding seepage pressure severity level.

[0031] The severity levels of seepage pressure include safety level, warning level, and severity level; the range of seepage pressure values ​​corresponding to each severity level can be set by referring to the industry standards for seepage technology in dam engineering to extract a common range of seepage pressure values.

[0032] When setting the density of monitoring points for each level of seepage pressure severity, since similar dam types have similar seepage pressure distribution characteristics and structural stress logic in engineering experience, the density of monitoring points corresponding to each level of seepage pressure severity can be directly used as the basis.

[0033] The core objective of monitoring at the severe seepage pressure level (safe level) is basic coverage to avoid redundancy. For similar dam types in areas at the safe level, the density of conventional monitoring points is 0.1 per square meter. The core objective of monitoring at the early warning level is to balance accuracy and cost, with a density of 0.2 per square meter. The core objective of monitoring at the severe level is to increase the density of monitoring to ensure no hidden dangers are missed, with a density of 0.3 per square meter.

[0034] The step of setting up monitoring points on the dam surface based on the boundary contours of key structural areas and historical seepage areas includes: importing the boundary contour coordinates of key structural areas and historical seepage areas into a GIS system and identifying the overlapping areas between them.

[0035] If a critical structural area overlaps with a historical seepage pressure area, the monitoring point density of the critical structural area and the historical seepage pressure area are compared, and the larger of the two values ​​is taken as the monitoring point density of the overlapping area. If there is no overlap between any critical structural area and any historical seepage pressure area, monitoring points are deployed on the surface of the dam body according to the monitoring point density corresponding to each critical structural area and each historical seepage pressure area.

[0036] It should be added that, in the GIS system, the vector surface layer of each key structural area is set as input element 1, and the vector surface layer of each historical seepage area is set as input element 2. The system automatically calculates the spatial intersection of the two types of areas and generates an overlapping area vector surface layer. If the generated overlapping area layer contains valid vector surfaces, that is, the area is greater than zero, it is determined that the key structural area and the historical seepage area have an overlapping area. If the generated overlapping area layer does not have valid vector surfaces, that is, the area is equal to zero, it is determined that the key structural area and the historical seepage area do not have an overlapping area.

[0037] Initial seepage pressure values ​​and environmental impact factors were collected at each monitoring point on the dam surface. The similarity of environmental impact factors between each monitoring point and all adjacent monitoring points within its set range was analyzed. Monitoring points with similarity greater than the set similarity threshold were clustered to form monitoring sub-regions.

[0038] This invention optimizes the density of monitoring points by combining the boundary contours of key structural areas and historical seepage pressure areas. This ensures basic monitoring needs in key structural areas while strengthening monitoring intensity in historically high-risk areas, avoiding waste of sensor resources, eliminating monitoring blind spots, and improving the rationality of monitoring coverage.

[0039] Please see Figure 2 As shown, the similarity analysis of environmental impact factors between each monitoring point and all its adjacent monitoring points includes: performing Z-score standardization on the upstream water level, downstream water level, and dam body temperature of the environmental impact factors of each monitoring point, and constructing the upstream water level, downstream water level, and dam body temperature of each monitoring point into environmental feature vectors of each monitoring point using the standardized upstream water level, downstream water level, and dam body temperature.

[0040] The cosine similarity algorithm was used to calculate the similarity of environmental influencing factors between each monitoring point and its neighboring monitoring points.

[0041] It should be noted that if the unprocessed environmental impact factor values ​​are used directly for calculation, the inconsistency in the units of measurement will make direct comparison impossible. Z-score standardization can convert the upstream water level, downstream water level, and dam temperature of the environmental impact factors into dimensionless data with a mean of 0 and a standard deviation of 1, which can be directly used in similarity calculation.

[0042] The step of clustering monitoring points with similarity greater than a set similarity threshold to form monitoring sub-regions includes: randomly selecting an unclassified monitoring point and finding all adjacent monitoring points within its set range and with similarity greater than a set similarity threshold as monitoring points of the same type.

[0043] If the number of adjacent monitoring points is greater than or equal to the minimum number of monitoring points contained in a preset sub-region, then the monitoring point and all similar monitoring points are divided into a monitoring sub-region.

[0044] Repeat the above steps for the remaining unclassified monitoring points until all monitoring points are classified.

[0045] It should be noted that the process of setting the similarity threshold is as follows: Based on the similarity of environmental impact factors between all monitoring points and their neighboring monitoring points, the distribution characteristics of similarity are statistically analyzed through a frequency distribution histogram. The environmental impact factor similarity value corresponding to the inflection point of the similarity distribution is found. If the environmental impact factor similarity value is greater than the environmental impact factor similarity value corresponding to the inflection point, the number of neighboring monitoring points is concentrated; otherwise, when the number of neighboring monitoring points decreases sharply, the environmental impact factor similarity value corresponding to the inflection point is used as the similarity threshold.

[0046] The number of monitoring points contained in the preset sub-region can be set to at least 3 to avoid correction deviations caused by abnormal environment at a single point or accidental similarity between the environments at two points in the existence of a sub-region formed by a single point or two points.

[0047] If the actual seepage pressure values ​​of all monitoring points are calculated together, the normal seepage pressure threshold will differ due to environmental differences. For example, the normal seepage pressure values ​​in the dam body water level fluctuation zone and the non-fluctuation zone are significantly different, and the normal differences caused by the environment will be mistakenly judged as abnormal. Therefore, clustering each monitoring point to form a monitoring sub-region can ensure that each monitoring point in the same monitoring sub-region is affected by environmental factors to the same extent, thereby improving the reliability of abnormal monitoring point judgment.

[0048] A pressure correction and compensation model is constructed. The initial pressure values ​​of all monitoring points in each monitoring sub-region and the environmental influencing factors are substituted into the pressure correction and compensation model, and the actual pressure values ​​of each monitoring point are output.

[0049] The construction of the seepage pressure correction and compensation model includes: constructing a seepage pressure simulation experiment of the dam body under the influence of the environment; monitoring the monitored seepage pressure value, actual seepage pressure value, and environmental influencing factors at each collection point in each seepage pressure simulation experiment; calculating the difference between the monitored seepage pressure value and the actual seepage pressure value at each collection point to obtain the seepage pressure interference value at each collection point; standardizing the seepage pressure interference value at each collection point, the upstream water level of the dam, the downstream water level of the dam, and the dam body temperature in each seepage pressure simulation experiment; using the standardized upstream water level of the dam, the downstream water level of the dam, and the dam body temperature as inputs, and using the seepage pressure interference value as output to fit a linear regression equation; calculating the constant term of the linear regression equation, the corresponding coefficient terms of the upstream water level of the dam, the downstream water level of the dam, and the dam body temperature based on the least squares method, and constructing the seepage pressure correction and compensation model.

[0050] It should be noted that the seepage pressure interference value is the seepage pressure measurement error caused by the upstream water level, downstream water level, and dam body temperature among the environmental influencing factors.

[0051] The specific calculation formula for the seepage pressure correction compensation model is as follows: .

[0052] in This is the permeability interference value. The water level upstream of the dam. This refers to the water level downstream of the dam. For the dam body temperature, This is the constant term in the linear regression equation. , , These are the corresponding coefficients for the upstream water level, downstream water level, and dam body temperature of the dam.

[0053] The output of the actual seepage pressure value of each monitoring point includes: substituting the upstream water level, downstream water level and dam body temperature of all monitoring points in each monitoring sub-region into the constructed seepage pressure correction and compensation model to obtain the seepage pressure interference value of each monitoring point, and determining the actual seepage pressure value of each monitoring point by calculating the difference between the initial seepage pressure value and the seepage pressure interference value of all monitoring points in the monitoring sub-region.

[0054] This invention takes into account the regional differences in environmental impact factors, standardizes these factors, and clusters each monitoring point into monitoring sub-regions based on the similarity of the environmental impact factors. The initial seepage pressure values ​​of all monitoring points in each monitoring sub-region and the environmental impact factors are substituted into the seepage pressure correction and compensation model to output the actual seepage pressure values ​​of each monitoring point. This can eliminate the interference of environmental impact factors on the actual seepage pressure values, enabling the data to accurately reflect the seepage pressure state of the dam itself and providing an accurate data basis for anomaly identification.

[0055] The actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region are compared to identify abnormal monitoring points. The location of the seepage pressure monitoring points is determined by combining the historical seepage pressure value time series of the abnormal monitoring points, and feedback is provided.

[0056] Please see Figure 3 As shown, the step of collaboratively comparing the actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region to determine abnormal monitoring points includes: calculating the arithmetic mean and standard deviation of the actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region; determining the abnormal judgment interval by combining the arithmetic mean and standard deviation of the actual seepage pressure values; and marking the monitoring point as an abnormal monitoring point if the actual seepage pressure value of a certain monitoring point exceeds the abnormal judgment interval.

[0057] It should be noted that the arithmetic mean of the actual seepage pressure values ​​of all monitoring points within the same monitoring sub-region minus twice the standard deviation is used as the lower limit of the anomaly judgment interval, and the arithmetic mean plus twice the standard deviation is used as the upper limit of the anomaly judgment interval. This constitutes the anomaly judgment interval for the actual seepage pressure values ​​of each monitoring sub-region.

[0058] The process of determining the location of the seepage pressure monitoring point and providing feedback includes: obtaining the historical seepage pressure value time series of the abnormal monitoring point, using linear regression to fit the time series curve, obtaining the curve change trend of the historical seepage pressure value of each abnormal monitoring point, and recording the maximum seepage pressure value of each abnormal monitoring point.

[0059] When the curve corresponding to an abnormal monitoring point tends to stabilize, if the maximum seepage pressure value when it reaches the stable trend exceeds the preset normal seepage pressure threshold, then the abnormal monitoring point is determined to be a seepage pressure monitoring point; otherwise, the abnormal monitoring point is determined to be a normal monitoring point.

[0060] When the curve corresponding to an abnormal monitoring point shows an upward trend and its maximum seepage pressure value exceeds the preset normal seepage pressure threshold, then the abnormal monitoring point is designated as a seepage pressure monitoring point.

[0061] Determine the location of the seepage pressure monitoring point corresponding to each abnormal monitoring point and provide feedback.

[0062] It should be noted that a linear regression equation is constructed with time as the independent variable and historical seepage pressure as the dependent variable. Curve fitting is performed based on the linear regression equation, and the trend of seepage pressure over time is determined by the slope of the curve. A curve slope of zero indicates a flat trend, while a curve slope of a positive number indicates an increasing trend.

[0063] The process of setting the normal seepage pressure threshold is as follows: the mean and standard deviation of the historical seepage pressure values ​​are calculated by obtaining the time series of historical seepage pressure values ​​of abnormal monitoring points, and the mean plus twice the standard deviation is used as the normal seepage pressure threshold.

[0064] This invention identifies abnormal monitoring points by collaboratively determining the actual seepage pressure values ​​of monitoring points within the same monitoring sub-region. It also determines the location of abnormal monitoring points by combining the historical seepage pressure value time series of the abnormal monitoring points and provides feedback. This avoids misjudging short-term normal fluctuations as abnormalities and missing potential hazards that grow slowly over a long period of time. It significantly reduces the misjudgment rate and missed judgment rate of anomaly identification and ensures the accuracy and reliability of dam safety monitoring.

[0065] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0066] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0067] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0069] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0070] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring and analyzing dam seepage pressure based on sensor data, characterized in that, include: The key structural areas on the surface of the dam body are identified, and they are divided into grids to determine the density of monitoring points in the key structural areas. Historical data of each historical seepage pressure area was extracted from the historical seepage pressure dataset of the dam body. The severity level of seepage pressure in each historical seepage pressure area was analyzed, the density of monitoring points in each historical seepage pressure area was determined, and monitoring points were set up on the surface of the dam body according to the boundary contours of the key structural areas and each historical seepage pressure area. Initial seepage pressure values ​​and environmental influencing factors were collected at various monitoring points on the dam surface. The similarity of environmental influencing factors between each monitoring point and all adjacent monitoring points within a set range was analyzed. Monitoring points with similarity values ​​greater than a set similarity threshold were clustered to form monitoring sub-regions. A seepage pressure correction and compensation model is constructed. The initial seepage pressure values ​​of all monitoring points in each monitoring sub-region and environmental influencing factors are substituted into the seepage pressure correction and compensation model, and the actual seepage pressure values ​​of each monitoring point are output. The actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region are compared to identify abnormal monitoring points. The location of the seepage pressure monitoring points is determined by combining the historical seepage pressure value time series of the abnormal monitoring points, and feedback is provided.

2. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The method of dividing the key structural area into grids and setting up several monitoring points includes: Based on the dam body structure layout diagram, obtain the key structural regions and corresponding structural function classifications on its surface. Obtain the dam's three-dimensional point cloud data by performing a three-dimensional scan on the dam body surface. Extract the boundary contour coordinates of each key structural region to form a closed regional vector graphic. Preset the corresponding grid unit spacing according to the structural function classification of each key structural region. Divide the regional vector graphics of each key structural region into a grid and use the geometric center of each grid unit as the location of the monitoring point. The density of monitoring points in key structural areas is determined by calculating the ratio of the number of monitoring points in each key structural area to the area of ​​the key structural area.

3. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The determination of the monitoring point density for each historical seepage pressure zone includes: Match the historical seepage pressure values ​​in the historical data of each historical seepage pressure area with the seepage pressure value range corresponding to each set seepage pressure severity level to obtain the seepage pressure severity level corresponding to each historical seepage pressure value, and count the percentage of seepage pressure severity level in each historical seepage pressure area. The seepage pressure severity level with the highest percentage is taken as the seepage pressure severity level of each historical seepage pressure area. The monitoring point density for each historical seepage pressure area is determined by matching the preset monitoring point density with the corresponding seepage pressure severity level.

4. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The method involves deploying monitoring points on the dam surface based on the boundary contours of key structural areas and historical seepage pressure areas, including: Import the boundary contour coordinates of each key structural area and each historical seepage pressure area into the GIS system to identify the overlapping areas between them. If a critical structural area overlaps with a historical seepage pressure area, the monitoring point density of the critical structural area and the historical seepage pressure area are compared, and the larger of the two values ​​is taken as the monitoring point density of the overlapping area. If there is no overlap between any critical structural area and any historical seepage pressure area, monitoring points are deployed on the surface of the dam body according to the monitoring point density corresponding to each critical structural area and each historical seepage pressure area.

5. The dam seepage pressure monitoring and analysis method based on sensor data as described in claim 1, characterized in that: The similarity analysis of environmental impact factors between each monitoring point and all its adjacent monitoring points includes: Z-score standardization was performed on the upstream water level, downstream water level, and dam body temperature of the environmental impact factors at each monitoring point. The standardized upstream water level, downstream water level, and dam body temperature were then used to construct the environmental feature vectors for each monitoring point. The cosine similarity algorithm was used to calculate the similarity of environmental influencing factors between each monitoring point and its neighboring monitoring points.

6. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The process of clustering monitoring points with similarity greater than a set similarity threshold to form monitoring sub-regions includes: Randomly select an unclassified monitoring point, and identify all adjacent monitoring points within its set range that have an environmental impact factor similarity greater than the set similarity threshold as monitoring points of the same type; If the number of adjacent monitoring points is greater than or equal to the minimum number of monitoring points contained in the preset sub-region, then the monitoring point and all similar monitoring points are divided into a monitoring sub-region. Repeat the above steps for the remaining unclassified monitoring points until all monitoring points are classified.

7. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The construction of the seepage pressure correction compensation model includes: Construct a seepage pressure simulation experiment of the dam body under the influence of the environment, and monitor the monitored seepage pressure value, actual seepage pressure value and environmental influencing factors at each collection point in each seepage pressure simulation experiment; The difference between the monitored seepage pressure value and the actual seepage pressure value at each collection point is calculated to obtain the seepage pressure interference value at each collection point. The seepage interference values, upstream water level, downstream water level and dam body temperature at each collection point in each seepage pressure simulation experiment were standardized, and the standardized upstream water level, downstream water level and dam body temperature were used as inputs. Using the seepage pressure disturbance value as the output, a linear regression equation is fitted. Based on the least squares method, the constant term of the linear regression equation, the corresponding coefficients of the upstream water level, the downstream water level, and the dam body temperature are calculated, and a seepage pressure correction and compensation model is constructed from them.

8. The dam seepage pressure monitoring and analysis method based on sensor data as described in claim 7, characterized in that: The output of the actual seepage pressure values ​​at each monitoring point includes: The upstream water level, downstream water level, and dam temperature of all monitoring points in each monitoring sub-region are substituted into the constructed seepage pressure correction and compensation model to obtain the seepage pressure interference value of each monitoring point. The actual seepage pressure value of each monitoring point is determined by calculating the difference between the initial seepage pressure value and the seepage pressure interference value of all monitoring points in the monitoring sub-region.

9. The method for monitoring and analyzing dam seepage pressure based on sensor data according to claim 1, characterized in that: The step of collaboratively comparing the actual seepage pressure values ​​of all monitoring points in the same monitoring sub-region to identify abnormal monitoring points includes: Calculate the arithmetic mean and standard deviation of the actual seepage pressure values ​​of all monitoring points within the same monitoring sub-region. Determine the anomaly judgment interval by combining the arithmetic mean and standard deviation of the actual seepage pressure values. If the actual seepage pressure value of a certain monitoring point exceeds the anomaly judgment interval, then mark the monitoring point as an abnormal monitoring point.

10. The dam seepage pressure monitoring and analysis method based on sensor data according to claim 1, characterized in that: The process of determining the location of the seepage pressure monitoring point and providing feedback includes: The historical seepage pressure value time series of the abnormal monitoring points is obtained, and the time series is curve-fitted by linear regression to obtain the curve change trend of the historical seepage pressure value of each abnormal monitoring point and record the maximum seepage pressure value of each abnormal monitoring point. When the curve corresponding to an abnormal monitoring point tends to stabilize, if the maximum seepage pressure value when it reaches the stable trend exceeds the preset normal seepage pressure threshold, then the abnormal monitoring point is determined to be a seepage pressure monitoring point; otherwise, the abnormal monitoring point is determined to be a normal monitoring point. When the curve corresponding to an abnormal monitoring point shows an upward trend and its maximum seepage pressure value exceeds the preset normal seepage pressure threshold, then the abnormal monitoring point is designated as a seepage pressure monitoring point. Determine the location of the seepage pressure monitoring point corresponding to each abnormal monitoring point and provide feedback.

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