Osmotic pressure monitoring data processing method and system for reservoir dam

Abstract

The invention relates to the technical field of dam safety monitoring, and discloses an osmotic pressure monitoring data processing method and system for a reservoir dam, and the method comprises the following steps: obtaining osmotic pressure monitoring data and a reservoir water level; based on the data, reconstructing and carrying out normalization processing to obtain a normalized potential energy field; constructing a topological network based on the normalized potential energy field, and calculating a seepage field topological entropy capable of representing the overall structural characteristics of the seepage field; recognizing a hydraulic step event triggered by the rapid change of the water level of the reservoir, collecting a topological entropy response curve of the seepage field under the event, and extracting a real-time response feature vector from the curve; the invention further provides a system for executing the method. According to the method, severe data fluctuation caused by normal working conditions (such as water level sudden change) and early weak abnormity caused by internal structural diseases can be effectively distinguished, and the sensitivity and accuracy of seepage state abnormity diagnosis are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dam safety monitoring, and in particular to a method and system for processing seepage pressure monitoring data for reservoir dams. Background Art

[0002] Reservoir dams are critical infrastructure crucial to the nation's economy and people's livelihoods, and their operational safety is paramount. Seepage is a key factor affecting dam safety and stability. Therefore, long-term, effective monitoring and analysis of the seepage conditions within a dam is crucial for ensuring its safe operation. Seepage pressure is the most direct physical quantity reflecting the state of the dam's internal seepage field, and analysis and interpretation of its monitoring data are the primary basis for evaluating dam health.

[0003] Currently, methods for analyzing dam seepage pressure monitoring data primarily rely on statistical analysis of data from a single or multiple measuring points. These methods typically compare real-time monitoring values ​​with historical data or predicted values ​​from statistical regression models based on environmental variables such as reservoir water levels. Anomalies are identified by determining whether deviations exceed specified limits. However, these methods have inherent limitations in practical applications.

[0004] These traditional analysis methods often focus on numerical changes at individual measuring points, making them essentially localized assessments that fail to capture the structural evolution of the entire seepage field from a global perspective. Early-stage defects within the dam, such as the initiation and expansion of tiny cracks or localized siltation in the drainage body, may initially cause only subtle distortions in the overall seepage field structure. These distortions are not noticeable in the readings at individual measuring points and are easily lost in normal fluctuation noise, leading to missed or delayed detection of defects.

[0005] Furthermore, when experiencing dramatic hydraulic changes, such as floods or rapid rises and falls in reservoir water levels, the dam's seepage field produces a strong dynamic response, causing readings at all measuring points to fluctuate significantly. Traditional diagnostic methods based on static or quasi-static thresholds struggle to effectively distinguish the dam's normal dynamic response from abnormal dynamic responses caused by internal defects under these conditions, often leading to numerous false alarms or failing to identify true abnormal signals obscured by the dramatic dynamic processes. Therefore, existing technologies are insufficient in their ability to provide a holistic and structural description of the seepage field and to perform accurate diagnosis under complex dynamic conditions. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a seepage pressure monitoring data processing method and system for reservoir dams, aiming to solve the technical problem in the existing technology that it is difficult to effectively distinguish between drastic data fluctuations caused by changes in normal operating conditions and early weak anomalies caused by internal structural diseases.

[0007] In order to solve the above technical problems, the present invention provides a new seepage pressure monitoring data processing method and system for reservoir dams.

[0008] A first aspect of the present invention provides a method for processing seepage pressure monitoring data for a reservoir dam, the method comprising: S1. Obtaining the seepage pressure monitoring data of multiple seepage pressure measuring points of the dam and their corresponding three-dimensional spatial coordinates, as well as the reservoir water level at the current moment; S2. reconstructing a continuous pressure field covering the monitoring area based on the seepage pressure monitoring data and the three-dimensional spatial coordinates, and normalizing the continuous pressure field in combination with the reservoir water level to obtain a normalized potential energy field; S3. constructing a topological network based on the normalized potential energy field, and calculating the seepage field topological entropy of the topological network; S4, identifying a hydraulic step event in which the rate of change of the reservoir water level exceeds a preset threshold; S5. In response to the hydraulic step event, collecting the seepage field topological entropy during and after the event to form a seepage field topological entropy response curve; S6. extracting a real-time response feature vector from the seepage field topological entropy response curve; S7, comparing the real-time response characteristic vector with a health response characteristic vector corresponding to the current hydraulic step event condition in a preset health response characteristic library; S8. Based on the comparison results, determine whether there is any abnormality in the seepage state of the dam.

[0009] Through the technical solution provided by the present invention, discrete monitoring data is converted into a topological entropy indicator that characterizes the overall structure of the seepage field. Natural hydraulic step events occurring during reservoir operation are used as system input. By analyzing the dynamic response characteristics of topological entropy, a quantitative assessment of the health status of the seepage system inside the dam is achieved, and structural anomalies that are difficult to detect using conventional static analysis methods can be identified.

[0010] In a specific embodiment, the step of constructing a topological network based on the normalized potential energy field includes: Extracting a series of equipotential surfaces from the normalized potential energy field, and determining each equipotential surface as a node in the topological network; When two equipotential surfaces are adjacent in space, an edge is established between the corresponding two nodes; The reciprocal of the hydraulic gradient between the two equipotential surfaces is determined as the weight of the corresponding edge.

[0011] In a specific embodiment, the step of calculating the topological entropy of the seepage field of the topological network includes: for each node in the topological network, calculating its normalized importance ; The seepage field topological entropy The normalized importance of all nodes in the topological network is calculated using the information entropy formula: ; in, is the total number of nodes in the topological network.

[0012] Preferably, the normalized importance of the node , is based on the comprehensive influence corresponding to the node Determined by the proportion of the total comprehensive influence of all nodes: ; Among them, the comprehensive influence The area of ​​the equipotential surface represented by the node The average connection weight to the node Determined jointly, the calculation formula is: .

[0013] Preferably, the step of identifying the hydraulic step event is specifically: calculating the change rate of the reservoir water level in real time, and when the absolute value of the change rate of the reservoir water level is greater than a preset water level change rate threshold, it is determined that a hydraulic step event has occurred.

[0014] Preferably, the response feature vector includes at least one of the following: Response delay, which is the time from the start of the hydraulic step event to the start of a significant change in the seepage field topological entropy response curve; A response peak value, which is the maximum value of the seepage field topological entropy response curve; Peak time, which is the time from the start of the hydraulic step event to the occurrence of the response peak; Decay time, which is the time required for the seepage field topological entropy response curve to decay from a peak value to a preset ratio; The response impulse is the area enclosed by the seepage field topological entropy response curve and the baseline.

[0015] Preferably, the step of comparing the real-time response feature vector with the health response feature vector in a preset health response feature library is: include: Calculate the real-time response feature vector With the health response feature vector Mahalanobis distance between : ;

[0016] in, is the covariance matrix of the health response eigenvector; the Mahalanobis distance Compare with the preset diagnostic threshold to determine abnormality.

[0017] In a specific embodiment, the method further includes: when no hydraulic step event is identified, comparing the seepage field topological entropy calculated in real time with a preset static normal range under the current reservoir water level, and judging whether there is any abnormality in the seepage state of the dam based on the comparison result.

[0018] In a specific embodiment, the method for constructing the preset health response feature library includes: Identify hydraulic step events that have occurred throughout the dam's healthy operating cycle; For each hydraulic step event, the corresponding seepage field topological entropy response curve is extracted, and a healthy response characteristic vector is calculated; The event condition of the hydraulic step event and the health response feature vector are stored as paired data to form the preset health response feature library.

[0019] A second aspect of the present invention provides a seepage pressure monitoring data processing system for a reservoir dam, the system comprising: a data processing module for acquiring seepage pressure monitoring data of a plurality of seepage pressure measuring points of the dam and their corresponding three-dimensional spatial coordinates and the reservoir water level at a current moment, reconstructing a continuous pressure field based on the seepage pressure monitoring data and the three-dimensional spatial coordinates, and then normalizing the continuous pressure field in combination with the reservoir water level to obtain a normalized potential energy field; A topological entropy calculation module, configured to construct a topological network based on the normalized potential energy field and calculate the topological entropy of the seepage field of the topological network; a dynamic response analysis module, configured to identify a hydraulic step event in which the rate of change of the reservoir water level exceeds a preset threshold, and in response to the hydraulic step event, control the topological entropy calculation module to collect the seepage field topological entropy during and after the event to form a seepage field topological entropy response curve, and extract a real-time response feature vector from the seepage field topological entropy response curve; The abnormality diagnosis module is used to compare the real-time response characteristic vector with the healthy response characteristic vector corresponding to the current hydraulic step event condition in a preset healthy response characteristic library, and based on the comparison result, determine whether there is any abnormality in the dam seepage state.

[0020] The present invention provides a method and system for processing seepage pressure monitoring data for reservoirs and dams. The method has the following beneficial effects: 1. By constructing a topological network of the seepage field and calculating its topological entropy, this method transforms discrete, local monitoring data into a global indicator that characterizes the overall structural complexity and order of the seepage field. Compared to methods that rely solely on single-point measurements or simple interpolation, this indicator is more sensitive to overall structural distortions of the seepage field caused by early-stage internal structural defects (such as microcracks or localized blockages), thereby improving the sensitivity and timeliness of anomaly identification.

[0021] 2. This method utilizes naturally occurring hydraulic step events during reservoir operation as a trigger for dynamic diagnosis and captures the response curve of the seepage field topological entropy for analysis. This method effectively distinguishes between normal system responses caused by drastic changes in reservoir water levels and abnormal responses caused by changes in the dam's internal seepage characteristics. By analyzing the response eigenvectors, the accuracy of diagnostic results is significantly improved under complex operating conditions, reducing the false alarm rate.

[0022] 3. This invention establishes a pre-defined health response signature library and employs statistical methods such as Mahalanobis distance for quantitative comparison, providing an objective and repeatable basis for assessing dam seepage status. This data-driven benchmarking approach avoids subjective judgments based on engineering experience or fixed, poorly applicable static thresholds, making diagnostic conclusions more reliable and laying the foundation for an automated, standardized health diagnosis process. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a method embodiment of the present invention; Figure 2 is a functional block diagram of a system embodiment of the present invention; Figure 3 Schematic diagram of the seepage field reconstruction and normalization process of the present invention; Figure 4 A schematic diagram of the process of constructing a topological network from a normalized potential energy field and calculating topological entropy according to the present invention; Figure 5 Schematic diagram of the dynamic response curve of the seepage field topological entropy of the present invention; Figure 6 Schematic diagram of the abnormality diagnosis process of the present invention.

[0024] Among them, 10, data processing module; 20, topological entropy calculation module; 30, dynamic response analysis module; 40, abnormality diagnosis module. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Refer to the attached Figure 1 , Figure 1 Flowchart of a method for processing seepage pressure monitoring data for a reservoir dam according to an embodiment of the present invention. The method provided by the present invention may include the following steps: S1. Obtain seepage pressure monitoring data of multiple seepage pressure measuring points of the dam and their corresponding three-dimensional spatial coordinates, as well as the reservoir water level at the current moment.

[0027] S2. Reconstruct a continuous pressure field covering the monitoring area based on the seepage pressure monitoring data and the three-dimensional space coordinates, and normalize the continuous pressure field in combination with the reservoir water level to obtain a normalized potential energy field.

[0028] S3. Construct a topological network based on the normalized potential energy field, and calculate the seepage field topological entropy of the topological network.

[0029] S4. Identify a hydraulic step event in which the reservoir water level change rate exceeds a preset threshold.

[0030] S5. In response to the hydraulic step event, collect the seepage field topological entropy during the period when the event occurs and a period of time thereafter to form a seepage field topological entropy response curve.

[0031] S6. Extracting a real-time response characteristic vector from the seepage field topological entropy response curve.

[0032] S7. Compare the real-time response characteristic vector with a health response characteristic vector in a preset health response characteristic library corresponding to the current hydraulic step event condition.

[0033] S8. Based on the comparison results, determine whether there is any abnormality in the dam seepage state.

[0034] Refer to the attached Figure 2 , Figure 2 This is a functional block diagram of a seepage pressure monitoring data processing system for a reservoir dam according to an embodiment of the present invention. Figure 1 The method shown in the figure includes: a data processing module 10, a topological entropy calculation module 20, a dynamic response analysis module 30 and an abnormality diagnosis module 40.

[0035] The data processing module 10 is used to execute steps S1 and S2 of the above method. Specifically, this module is used to obtain the seepage pressure monitoring data, three-dimensional spatial coordinates, and reservoir water level information of multiple seepage pressure measurement points on the dam, and reconstruct and normalize the continuous pressure field based on this information, and finally output the normalized potential energy field to the topological entropy calculation module 20.

[0036] The topological entropy calculation module 20 is used to perform step S3 of the above method. This module receives the normalized potential energy field generated by the data processing module 10, constructs a topological network based on this field data, and calculates the seepage field topological entropy that characterizes the structural characteristics of the seepage field at the current moment. This module outputs the calculated seepage field topological entropy to the dynamic response analysis module 30 and the abnormality diagnosis module 40.

[0037] The dynamic response analysis module 30 is used to perform steps S4, S5, and S6 of the above method. This module is used to identify hydraulic step events. When an event occurs, it continuously obtains the seepage field topological entropy from the topological entropy calculation module 20 to form a response curve and extracts a real-time response feature vector from this curve. This module outputs the extracted response feature vector to the abnormality diagnosis module 40.

[0038] Abnormality diagnosis module 40 is used to perform steps S7 and S8 in the above method. This module receives the real-time response feature vector generated by dynamic response analysis module 30 and quantitatively compares it with the healthy response feature vectors in a preset healthy response feature library. Based on the comparison results, this module ultimately outputs a conclusion regarding whether the dam's seepage status is abnormal.

[0039] In one embodiment, the data processing module 10, the topology entropy calculation module 20, the dynamic response analysis module 30, and the anomaly diagnosis module 40 can be implemented as software program modules running on one or more processors, including but not limited to a server, a workstation, or an embedded computing device. These modules communicate data and call functions via predefined application programming interfaces (APIs).

[0040] Refer to the attached Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a seepage field reconstruction and normalization process according to an embodiment of the present invention. The specific implementation of steps S1 and S2 in the method of the present invention is as follows.

[0041] First, execute step S1 to obtain the basic data required for calculation. This step obtains the basic data required for calculation at the current moment by interfacing with the automatic data acquisition unit or manual observation database of the dam safety monitoring system. The dataset includes: The osmotic pressure monitoring values ​​of each osmotic pressure measuring point , and each measuring point Three-dimensional space coordinates in the dam coordinate system ,in At the same time, obtain the current water level value of the upstream reservoir After acquiring the data, data cleaning operations can be performed to process invalid data or abnormal values ​​caused by sensor failure or transmission errors, and all data units can be unified, such as unifying the pressure unit to kilopascals (kPa) and the coordinate and water level units to meters (m).

[0042] Then, step S2 is executed to generate a continuous field covering the entire monitoring area based on the discrete measurement point data obtained in step S1, and normalize it. This step can be specifically divided into two sub-steps: continuous pressure field reconstruction and normalization. In the continuous pressure field reconstruction sub-step, a spatial interpolation algorithm is used to calculate the pressure field according to the pressure field. The pressure value and spatial coordinates of each measuring point are calculated to obtain the coordinates of any point in the monitoring area. In a specific embodiment, the Kriging interpolation method can be used. This method is an unbiased optimal estimation method based on spatial autocorrelation. Estimated pressure at It can be obtained by weighted linear combination of the pressure values ​​of the surrounding known measuring points: ; in, For the The measured pressure value of each measuring point; To allocate to Weight coefficient of each measuring point. The determination is based on the analysis of the spatial variation structure of the pressure field in the monitoring area (described by the variogram) so that the estimated value The estimated variance of is the smallest and satisfies the unbiased condition. By calculating all grid points in the monitoring area, a continuous three-dimensional pressure field can be generated.

[0043] In the normalization sub-step, in order to eliminate the direct impact of the absolute value fluctuation of the reservoir water level on the pressure field, the reconstructed continuous pressure field is converted into a normalized potential energy field. First, the pressure value of each point is Converted to piezoresistance head (or potential energy) Then, the head field is normalized using the following formula to obtain the normalized potential energy : ; in, for point Normalized potential energy at ; is the piezometric tube head corresponding to the point; is the upstream water level of the reservoir at the current moment, which serves as the upstream boundary condition; is the downstream water level or a fixed downstream reference elevation, which serves as the downstream boundary condition. After this step, the output normalized potential energy field is It is a dimensionless scalar field whose numerical distribution reflects only the hydraulic gradient distribution characteristics within the seepage field and is unaffected by changes in the absolute elevation of the upstream water level. This normalized potential energy field will serve as the basis for the subsequent construction of the topological network.

[0044] Refer to the attached Figure 4 , Figure 4 FIG. 1 is a schematic diagram of a process for constructing a topological network from a normalized potential energy field and calculating topological entropy according to an embodiment of the present invention. The specific implementation of step S3 in the method of the present invention is as follows.

[0045] This step receives the normalized potential field output by the previous step , and based on this, a topological network is constructed and the topological entropy of the seepage field is calculated. This step can be specifically divided into two sub-steps: topological network construction and seepage field topological entropy calculation.

[0046] In the topological network construction sub-step, the continuous normalized potential energy field is discretized and abstracted into a network model. First, the value range of the normalized potential energy (usually) is divided into consecutive, non-overlapping intervals. Each interval Corresponding to an equipotential surface set. In three-dimensional space, this set is represented as an equipotential surface or an equipotential body layer. The equipotential surface (or equipotential body layer) is abstracted as a node in the topological network, denoted as node .

[0047] Then, edges are created between the defined nodes. When the equipotential surfaces corresponding to two nodes are directly adjacent in space, an edge is created between the two nodes. For example, representing the potential energy interval Node and represents the potential energy interval Node are adjacent in space, so an edge is created between them.

[0048] Next, calculate the weight of each established edge. and nodes The weight of the edge It is defined as the reciprocal of the hydraulic gradient between the corresponding equipotential surfaces connecting the two nodes. In a specific embodiment, the hydraulic gradient is It can be calculated as the potential energy difference between two equipotential surfaces The average normal distance between The weight is The calculation formula is: ; in, represents weight; Indicates the magnitude of hydraulic gradient; It represents the potential energy difference between the centers of two equipotential surfaces; The weight represents the average normal distance between two equipotential surfaces, which can be obtained through computational geometry algorithms. The weight reflects the difficulty of water flowing through the area between the two equipotential surfaces.

[0049] In the seepage field topological entropy calculation sub-step, a single quantitative index is calculated based on the constructed weighted topological network. First, for each node in the network , calculate its comprehensive influence The influence is determined by the area of ​​the equipotential surface represented by the node The average connection weight to the node Jointly determine: ; in, For the The equipotential surface at time The spatial area of ​​can be obtained by summing the areas of the three-dimensional grid cells that constitute the equipotential surface; For nodes At the moment The average connection weight of , which is calculated as the arithmetic mean of the weights of all edges connected to the node.

[0050] Then, each node is calculated based on the comprehensive influence The normalized importance of This value is passed through the node The comprehensive influence of nodes is determined by the ratio of their comprehensive influence to the total comprehensive influence of all nodes in the network: ; in, is the total number of nodes in the network. It constitutes a probability distribution that describes the distribution characteristics of seepage energy in space. Finally, the information entropy formula is used to calculate the current moment The topological entropy of the seepage field : ; The logarithm operation here can be based on 2 or natural logarithm. The calculated topological entropy of the seepage field is It is a scalar value whose size reflects the complexity and uniformity of the seepage field structure of the entire dam. This entropy value will be output to the subsequent steps for dynamic response analysis.

[0051] Refer to the attached Figure 5 and attached Figure 6 , Figure 5 is a schematic diagram of a dynamic response curve of the seepage field topological entropy according to an embodiment of the present invention, Figure 6 FIG. 1 is a flow chart of an abnormality diagnosis process according to an embodiment of the present invention. The specific implementation of steps S4 to S8 in the method of the present invention is as follows.

[0052] First, step S4 is executed to identify the hydraulic step event. This step continuously obtains the current reservoir water level through a monitoring process. , and calculate its rate of change per unit time. In a specific embodiment, the rate of change can be obtained by differential calculation: The absolute value of the change rate is compared with a preset water level change rate threshold. When the value is greater than the threshold, the system determines that a hydraulic step event is triggered and records the starting time of the event. and event attributes, such as whether the water level rose or fell, and the magnitude of the change.

[0053] Next, step S5 is executed to collect and form a seepage field topological entropy response curve in response to the identified hydraulic step event. Once the event is triggered, the system controls the topological entropy calculation module to continuously calculate and record the seepage field topological entropy at a higher frequency (for example, every 5 minutes) during and after the event for a preset period of time (for example, for 24 hours or until the system reaches a stable state again). These entropy value sequences arranged in chronological order constitute the following Figure 5 A complete seepage field topological entropy response curve is shown.

[0054] Then, step S6 is executed to extract quantitative features from the response curve generated above. The system calculates and extracts one or more predefined response feature parameters from the curve and combines them into a real-time response feature vector In a specific embodiment, the vector may include: Response delay :From the start time of the event The duration from when the value of a response curve first exceeds a specified percentage (e.g. 5%) of its baseline fluctuation range.

[0055] Response peak : The maximum entropy value of the response curve during the entire response period.

[0056] Peak time :From the start time of the event To the peak response Duration of occurrence.

[0057] decay time :The response curve reaches its peak from After that, it decays to a preset ratio of the difference between the peak value and the baseline (for example The time that has passed.

[0058] Response impulse : The response curve is higher than its baseline value during the response period The area enclosed by the part and the time axis is calculated as follows: . These calculated scalar values ​​are combined into a multidimensional vector: .

[0059] Then, steps S7 and S8 are executed for comparison and diagnosis. The system first retrieves the health response feature vector that matches the conditions from a preset health response feature library based on the attributes of the current hydraulic step event (such as change amplitude and initial water level). and its corresponding covariance matrix Then, calculate the real-time response feature vector and health response feature vector Mahalanobis distance between : ; Among them, the superscript Indicates matrix transpose, superscript The calculation of Mahalanobis distance takes into account the correlation between each feature and performs normalization.

[0060] Finally, the calculated Mahalanobis distance The test result is compared with a pre-set diagnostic threshold. If the value is greater than the diagnostic threshold, the system determines that the seepage state of the dam is abnormal and generates an alarm signal; otherwise, If the value is less than or equal to the diagnostic threshold, the current state is judged to be normal.

[0061] In an optional embodiment, the method further includes a static diagnostic mode. During the stable operation period when no hydraulic step event is identified, the system calculates the topological entropy of the seepage field in real time. With a current reservoir water level Static normal range If the real-time entropy value exceeds the normal range, the system will make a preliminary judgment that there is an abnormality.

[0062] The preset health response feature library is constructed by selecting historical operating data of the dam when it is confirmed to be in a healthy state and identifying all hydraulic step events that have occurred. For each healthy event, its complete topological entropy response curve is extracted and the corresponding response feature vector is calculated. These healthy feature vectors are grouped according to event attributes, and the mean vector of each group of data is calculated (as ) and the covariance matrix (as ), and finally store the event attributes, mean vector and covariance matrix as a record in the library.

Claims

1. A method for processing seepage pressure monitoring data for a reservoir dam, characterized in that: The following steps are involved: S1. Obtaining the seepage pressure monitoring data of multiple seepage pressure measuring points of the dam and their corresponding three-dimensional spatial coordinates, as well as the reservoir water level at the current moment; S2. reconstructing a continuous pressure field covering the monitoring area based on the seepage pressure monitoring data and the three-dimensional spatial coordinates, and normalizing the continuous pressure field in combination with the reservoir water level to obtain a normalized potential energy field; S3. constructing a topological network based on the normalized potential energy field, and calculating the seepage field topological entropy of the topological network; S4, identifying a hydraulic step event in which the rate of change of the reservoir water level exceeds a preset threshold; S5. In response to the hydraulic step event, collecting the seepage field topological entropy during and after the event to form a seepage field topological entropy response curve; S6. extracting a real-time response feature vector from the seepage field topological entropy response curve; S7, comparing the real-time response characteristic vector with a health response characteristic vector corresponding to the current hydraulic step event condition in a preset health response characteristic library; S8. Based on the comparison results, determine whether there is any abnormality in the seepage state of the dam.

2. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The step of calculating the topological entropy of the seepage field of the topological network includes: For each node in the topological network, calculating its normalized importance; The seepage field topological entropy is calculated by the normalized importance of all nodes in the topological network using the information entropy formula.

3. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 2, characterized in that: The normalized importance of a node is determined based on the proportion of the node's comprehensive influence to the total comprehensive influence of all nodes. The comprehensive influence is determined by the area of ​​the equipotential surface represented by the node and the average connection weight of the node.

4. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The step of constructing a topological network based on the normalized potential energy field includes: Extracting a series of equipotential surfaces from the normalized potential energy field, and determining each equipotential surface as a node in the topological network; When two equipotential surfaces are adjacent in space, an edge is established between the corresponding two nodes; The reciprocal of the hydraulic gradient between the two equipotential surfaces is determined as the weight of the corresponding edge.

5. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The step of identifying the hydraulic step event is specifically: The change rate of the reservoir water level is calculated in real time, and when the absolute value of the change rate of the reservoir water level is greater than a preset water level change rate threshold, it is determined that a hydraulic step event has occurred.

6. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The response feature vector includes at least one of the following: Response delay, which is the time from the start of the hydraulic step event to the start of a significant change in the seepage field topological entropy response curve; A response peak value, which is the maximum value of the seepage field topological entropy response curve; Peak time, which is the time from the start of the hydraulic step event to the occurrence of the response peak; Decay time, which is the time required for the seepage field topological entropy response curve to decay from a peak value to a preset ratio; The response impulse is the area enclosed by the seepage field topological entropy response curve and the baseline.

7. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The step of comparing the real-time response feature vector with the health response feature vector in a preset health response feature library includes: Calculating the Mahalanobis distance between the real-time response feature vector and the healthy response feature vector; The Mahalanobis distance is compared with a preset diagnostic threshold to determine abnormality.

8. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The method further includes: When no hydraulic step event is identified, the seepage field topological entropy calculated in real time is compared with a preset static normal range under the current reservoir water level, and whether there is an abnormality in the dam seepage state is determined based on the comparison result.

9. The method for processing seepage pressure monitoring data for a reservoir dam according to claim 1, characterized in that: The method for constructing the preset health response feature library includes: Identify hydraulic step events that have occurred throughout the dam's healthy operating cycle; For each hydraulic step event, the corresponding seepage field topological entropy response curve is extracted, and a healthy response characteristic vector is calculated; The event condition of the hydraulic step event and the health response feature vector are stored as paired data to form the preset health response feature library.

10. A seepage pressure monitoring data processing system for reservoirs and dams, characterized in that: The method for processing seepage pressure monitoring data for a reservoir dam according to any one of claims 1 to 9 comprises: a data processing module for acquiring seepage pressure monitoring data of a plurality of seepage pressure measuring points of the dam and their corresponding three-dimensional spatial coordinates and the reservoir water level at a current moment, reconstructing a continuous pressure field based on the seepage pressure monitoring data and the three-dimensional spatial coordinates, and then normalizing the continuous pressure field in combination with the reservoir water level to obtain a normalized potential energy field; A topological entropy calculation module, configured to construct a topological network based on the normalized potential energy field and calculate the topological entropy of the seepage field of the topological network; a dynamic response analysis module, configured to identify a hydraulic step event in which the rate of change of the reservoir water level exceeds a preset threshold, and in response to the hydraulic step event, control the topological entropy calculation module to collect the seepage field topological entropy during and after the event to form a seepage field topological entropy response curve, and extract a real-time response feature vector from the seepage field topological entropy response curve; The abnormality diagnosis module is used to compare the real-time response characteristic vector with the healthy response characteristic vector corresponding to the current hydraulic step event condition in a preset healthy response characteristic library, and based on the comparison result, determine whether there is any abnormality in the dam seepage state.

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