Engineering anti-seepage monitoring method and system based on potential difference change

Abstract

The application discloses an engineering anti-seepage monitoring method and system based on potential difference change, relates to the related technical field of engineering monitoring, and comprises the following steps: arranging detection units on the anti-seepage layer of the target engineering anti-seepage area, and constructing an anti-seepage layer cross-interface potential detection network; acquiring multi-source environmental response data of the target engineering anti-seepage area; inputting the data into a dynamic compensation potential field model, and outputting a spatial potential compensation strategy; correcting a reference potential field to establish a real-time reference potential field; establishing a real-time response potential after acquiring detection data; performing differential offset analysis and cross-node correlation analysis through spatial topological relations, and establishing an abnormal early warning. The application solves the technical problems of response lag, low positioning accuracy, poor environmental adaptability, limited monitoring coverage range and difficulty in realizing real-time online monitoring in the prior art, and achieves the technical effects of improving the real-time performance of seepage response, the accuracy of seepage point positioning, the dynamic compensation capability for environmental disturbance and the long-term stability of engineering monitoring.

Description

Technical Field

[0001] This invention relates to the field of engineering monitoring technology, specifically to a method and system for monitoring engineering seepage prevention based on changes in potential difference. Background Technology

[0002] In solid waste landfills, hazardous waste disposal sites, and water conservancy projects, the integrity and reliability of the seepage prevention system are crucial for ensuring project safety, ecological and environmental safety, and operational compliance. Water bodies, leachate, or groundwater typically exist above or below the seepage prevention layer (such as HDPE, LLDPE, TPO, EVA, PE, etc.). Once the seepage prevention structure is damaged, pollutants or water can penetrate the layer, leading to leakage events. This can cause serious soil pollution, groundwater damage, ecosystem degradation, and even trigger project safety accidents. Current leakage detection mainly relies on manual inspections, periodic testing, or localized electrical resistivity tomography (EDT), which has many shortcomings. The detection cycle is long, making it difficult to detect potential problems in the initial stages of leakage, easily missing the optimal treatment window; it lacks precise spatial location capabilities for leak points, making it difficult to guide accurate repairs; and the external environment significantly interferes with the detection results, lacking an effective compensation mechanism. Current leakage detection based on electrical principles has failed to achieve stable, long-term, and real-time online monitoring in complex engineering environments. In particular, it lacks the ability to dynamically compensate for multi-source environmental disturbances, resulting in high false alarm rates, poor stability, and insufficient engineering adaptability in actual working conditions.

[0003] Therefore, current technologies suffer from technical problems such as slow response, low positioning accuracy, poor environmental adaptability, limited monitoring coverage, and difficulty in achieving real-time online monitoring. Summary of the Invention

[0004] This application provides an engineering seepage prevention monitoring method and system based on potential difference changes, which solves the technical problems of existing technologies such as slow response, low positioning accuracy, poor environmental adaptability, limited monitoring coverage, and difficulty in achieving real-time online monitoring. It achieves the technical effects of improving the real-time performance of seepage response, the accuracy of seepage point location, the dynamic compensation capability for environmental disturbances, and the long-term stability of engineering monitoring.

[0005] This application provides a method for monitoring seepage prevention in engineering projects based on potential difference changes. The method includes: deploying detection units in the seepage prevention layer of the target engineering seepage prevention area; constructing a cross-interface potential detection network for the seepage prevention layer using the spatial positional relationship of the detection units; continuously acquiring multi-source environmental response data during the operation of the seepage prevention area of ​​the target engineering project, including soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data; constructing an environmental disturbance feature set using the multi-source environmental response data; inputting the environmental disturbance feature set into a dynamic compensation potential field model and outputting a spatial potential compensation strategy; establishing a real-time reference potential field after correcting the reference potential field of the cross-interface potential detection network for the seepage prevention layer using the spatial potential compensation strategy; establishing a real-time response potential after acquiring the detection data of the corresponding detection units in the cross-interface potential detection network for the seepage prevention layer; performing differential offset analysis on the real-time response potential and the real-time reference potential field; and performing cross-node correlation analysis through the spatial topology relationship of the cross-interface potential detection network for the seepage prevention layer to establish an anomaly early warning.

[0006] In a possible implementation, the set of environmental disturbance features is input into a dynamic compensation potential field model, and a spatial potential compensation strategy is output. This includes: extracting spatial topological information of the seepage prevention area of ​​the target project using a cross-interface potential detection network of the seepage prevention layer. The spatial topological information includes spatial distribution features of the seepage prevention layer, spatial location features of the detection units, regional medium distribution features, and structural boundary constraint relationships; pre-inputting the spatial distribution features of the seepage prevention layer into the dynamic compensation potential field model to perform model initialization processing; inputting the set of environmental disturbance features into the initialized dynamic compensation potential field model, performing environmental disturbance drift influence analysis, and outputting a spatial potential compensation strategy.

[0007] In a possible implementation, the set of environmental disturbance features is input into the initialized dynamic compensation potential field model, including: using the structure identification channel in the dynamic compensation potential field model to determine the potential transmission path between adjacent spatial locations in the cross-interface potential detection network of the seepage barrier layer based on the spatial location characteristics of the detection unit, and using the regional medium distribution characteristics to calculate the equivalent conductivity parameters of each potential transmission path to form a reference potential correspondence; applying the soil water content data, temperature and humidity change data, regional conductivity change data, and external disturbance data in the set of environmental disturbance features to the equivalent conductivity parameter change at the corresponding spatial location, and calculating the influence of each environmental factor on the potential gradient change in the potential transmission path; activating the coupling analysis channel to analyze the combined effect of different environmental factors on the same potential transmission path based on the influence of each environmental factor on the potential transmission path, and determining the comprehensive change characteristics of the path potential transmission impedance; and using the comprehensive change characteristics to output a spatial potential compensation strategy.

[0008] In a possible implementation, activating the coupling analysis channel analyzes the combined effects of different environmental factors on the same potential transmission path based on the influence of each environmental factor on the potential transmission path. This includes: performing time-series alignment processing on the influence quantities corresponding to different environmental disturbance factors to obtain the change response characteristics of the equivalent conductance parameters of each environmental factor to the same potential transmission path within the same time window; calculating the consistency coefficient and difference coefficient of the potential gradient change in the same potential transmission path based on the change response characteristics of each environmental factor; performing weighted coupling correction on the change of the equivalent conductance parameters of the potential transmission path based on the consistency coefficient and difference coefficient, and synchronously updating the potential transmission relationship between adjacent spatial locations; and forming a coupled spatial potential compensation strategy based on the updated potential transmission relationship.

[0009] In a possible implementation, the construction of the reference potential field includes: determining the equivalent conductivity parameters between each detection unit based on the spatial positional relationship of each detection unit in the cross-interface potential detection network of the impermeable layer, combined with the regional medium distribution characteristics; calculating the steady-state potential distribution of the cross-interface potential detection network of the impermeable layer based on the equivalent conductivity parameters under calibration test conditions, generating the reference potential value of each detection unit; and performing consistency calibration of the reference potential values ​​according to the spatial positional relationship to form the reference potential field.

[0010] In possible implementation methods, an anomaly warning system is established, including: performing point-by-point differential calculations on the real-time response potential and real-time reference potential field based on the spatial location correspondence to establish the potential offset of each detection unit; performing correlation analysis of the potential offset in the spatial topological relationship to identify diffusion trend characteristics, and using the diffusion trend characteristics to establish an anomaly warning system.

[0011] In possible implementations, identifying diffusion trend characteristics includes: extracting the spatial adjacency relationship of each detection unit in the cross-interface potential detection network of the impermeable layer as a spatial topological relationship; using the spatial topological relationship to perform neighborhood aggregation processing on the potential offset of each detection unit to obtain the offset aggregation intensity of each detection unit in the local spatial range; and identifying the continuous propagation direction and diffusion path of the potential offset in the spatial topological relationship based on the difference in the offset aggregation intensity between adjacent detection units to generate diffusion trend characteristics.

[0012] In possible implementations, establishing an anomaly warning based on diffusion trend characteristics also includes: establishing a leakage source identifier after performing source tracing and location based on the potential offset; performing radiation correlation analysis of the leakage source using the diffusion trend characteristics to establish a radiation intensity path; configuring an anomaly warning signal according to the leakage source identifier and radiation intensity path, and issuing an early warning.

[0013] In one possible implementation, the detection unit is encapsulated in graphene and protected with HDPE material, and fixed to the structural layer of the seepage-proof area of ​​the target project by hot-melt method.

[0014] This application also provides an engineering seepage prevention monitoring system based on potential difference changes. The system includes: a detection network construction module, used to deploy detection units in the seepage prevention layer of the target engineering seepage prevention area, and construct a cross-interface potential detection network for the seepage prevention layer using the spatial relationship of the detection units; an environmental response data acquisition module, used to continuously acquire multi-source environmental response data during the operation of the target engineering seepage prevention area, the multi-source environmental response data including soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data, and construct an environmental disturbance feature set using the multi-source environmental response data; and a compensation strategy output module, used for... The environmental disturbance feature set is input into the dynamic compensation potential field model, and a spatial potential compensation strategy is output. The potential field correction module is used to correct the reference potential field of the cross-interface potential detection network of the seepage barrier layer using the spatial potential compensation strategy, and then establish a real-time reference potential field. The real-time response potential establishment module is used to establish a real-time response potential after acquiring the detection data of the corresponding detection unit in the cross-interface potential detection network of the seepage barrier layer. The anomaly early warning analysis module is used to perform differential offset analysis on the real-time response potential and the real-time reference potential field, and to perform cross-node correlation analysis through the spatial topology relationship of the cross-interface potential detection network of the seepage barrier layer to establish anomaly early warning.

[0015] This application proposes a method and system for monitoring engineering seepage prevention based on potential difference changes. The method involves deploying detection units in the seepage prevention layer of the target engineering project's seepage prevention area to construct a cross-interface potential detection network. It acquires multi-source environmental response data of the seepage prevention area, inputs this data into a dynamic compensation potential field model, and outputs a spatial potential compensation strategy. A real-time reference potential field is established by correcting the baseline potential field. A real-time response potential is established after acquiring the detection data. Differential offset analysis is performed, and cross-node correlation analysis is conducted through the spatial topology of the cross-interface potential detection network to establish anomaly early warning. This method solves the technical problems of existing technologies, such as slow response, low positioning accuracy, poor environmental adaptability, limited monitoring coverage, and difficulty in achieving real-time online monitoring. It achieves the technical effects of improving the real-time performance of seepage response, the accuracy of seepage point location, the dynamic compensation capability for environmental disturbances, and the long-term stability of engineering monitoring. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic diagram of the process for monitoring engineering seepage prevention based on potential difference changes, provided in an embodiment of this application.

[0018] Figure 2 A schematic diagram of the engineering seepage prevention monitoring system based on potential difference change provided in the embodiments of this application.

[0019] Figure labeling: Detection network construction module 10, environmental response data acquisition module 20, compensation strategy output module 30, electric potential field correction module 40, real-time response potential establishment module 50, and anomaly early warning analysis module 60. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0021] This application provides an engineering seepage prevention monitoring method based on potential difference changes, such as... Figure 1 As shown, the method includes:

[0022] Step S100: Install detection units in the seepage-proof layer of the target project's seepage-proof area, and construct a cross-interface potential detection network for the seepage-proof layer using the spatial relationship of the detection units.

[0023] Step S100 further includes encapsulating the detection unit with graphene and protecting it with HDPE material, and fixing it to the structural layer of the seepage prevention area of ​​the target project by hot-melt method.

[0024] Preferably, detection units are deployed in the seepage-proof layer of the target project's seepage-proof area. Specifically, detection units are installed above the insulating seepage-proof layer (such as an HDPE membrane), and feedback units are installed below the seepage-proof layer. The detection units are encapsulated in graphene and protected with HDPE material, and are fixed to the seepage-proof structural layer of the target project's seepage-proof area via thermal fusion, without damaging the seepage-proof structural layer. Specifically, multiple detection units are deployed according to a gridded or specific spatial topology, such as spacing them at 5-meter or 10-meter intervals. Then, using the spatial positional relationships of the detection units—that is, the specific coordinates of each detection unit on the seepage-proof layer and their relative positions (such as distance, adjacency, and whether they form a triangle for positioning)—a virtual detection network is logically constructed. Simultaneously, the potential difference between the detection units above the seepage-proof layer and the feedback units below the seepage-proof layer is monitored. The electrical signals of each independent detection unit are correlated according to their spatial positions to obtain a cross-interface potential detection network for the seepage-proof layer, covering the entire seepage-proof area. When leakage occurs, it affects the potential of multiple nearby detection units. Based on the location and degree of impact of the affected units (the magnitude of the potential difference), the coordinates of the leakage point can be accurately calculated.

[0025] Step S200: Continuously acquire multi-source environmental response data during the operation of the seepage prevention area of ​​the target project. The multi-source environmental response data includes soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data. Construct an environmental disturbance feature set using the multi-source environmental response data.

[0026] Preferably, real-time continuous data collection is performed using different sensors to collect soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data during the operation of the seepage prevention area of ​​the target project, thereby determining multi-source environmental response data. Among these, soil moisture content data refers to the water content in the soil or landfill material surrounding the seepage prevention layer, measured by a humidity sensor, as the conductivity of water directly affects the distribution of the electric potential field; temperature and humidity change data refers to the temperature and humidity of the ambient air and the soil near the seepage prevention layer, which also change the conductivity of the medium; regional conductivity change data refers to the overall conductivity of the medium above or below the seepage prevention layer, obtained by a conductivity sensor, to directly quantify the environmental conductivity; external disturbance data refers to external events that may affect stability, such as rainfall, compaction operations, seismic waves, and human construction, as disturbances can instantly change the physical field, such as compressing the soil and altering the moisture distribution. Then, the multi-source environmental response data is cleaned and aligned, that is, aligned according to a unified timestamp and corresponding spatial location. Then, key features that affect the electric potential field are extracted from it, such as the rate of temperature change, the difference between the current soil moisture and the historical average moisture, the duration of the rainfall event and the cumulative rainfall, and the intensity level of external disturbances. The extracted key features are then structured and encapsulated into an environmental disturbance feature set to provide data for dynamic compensation analysis, thereby significantly improving the anti-interference ability and alarm accuracy of leakage detection.

[0027] Step S300: Input the set of environmental disturbance features into the dynamic compensation potential field model and output the spatial potential compensation strategy.

[0028] Step S300 further includes extracting spatial topological information of the seepage prevention area of ​​the target project using a cross-interface potential detection network of the seepage prevention layer. The spatial topological information includes spatial distribution characteristics of the seepage prevention layer, spatial location characteristics of the detection unit, regional medium distribution characteristics, and structural boundary constraint relationships. The spatial distribution characteristics of the seepage prevention layer are pre-input into the dynamic compensation potential field model to perform model initialization processing. The environmental disturbance feature set is input into the initialized dynamic compensation potential field model to perform environmental disturbance drift influence analysis and output a spatial potential compensation strategy.

[0029] Preferably, the spatial topological information of the seepage prevention area of ​​the target project is extracted using a cross-interface potential detection network of the seepage prevention layer. This includes the spatial distribution characteristics of the seepage prevention layer, the spatial location characteristics of the detection units, the regional medium distribution characteristics, and the structural boundary constraints. Among these, the spatial distribution characteristics of the seepage prevention layer refer to the geometry, slope, partitions, and area of ​​the seepage prevention layer (such as HDPE membrane), which are used to define the physical boundary and basic form of the potential field. The spatial location characteristics of the detection units are the precise three-dimensional coordinates of each detection unit and feedback unit, as well as their relative distance and adjacency relationship, in order to calculate the potential transmission distance and diffusion trend. The regional medium distribution characteristics refer to the medium type (such as soil, gravel, leachate collection layer, and protective layer) and its thickness and uniformity in different areas above and below the seepage prevention layer. The same potential difference may represent different leakage risks in different media. The structural boundary constraints are the boundary conditions of the seepage prevention layer, such as anchor trenches, welds, and connections with the dam or pool wall, which are the "endpoints" or "abrupt points" of the potential field.

[0030] Preferably, the spatial distribution characteristics of the seepage-proof layer are pre-input into the dynamic compensation potential field model to perform model initialization processing, that is, to establish a dynamic compensation potential field model based on digital twin, including the geometry of the potential field, the wiring logic of the sensor network, the electrical properties of different regions, and the potential field boundary; then, the set of environmental disturbance characteristics is input into the dynamic compensation potential field model to perform environmental disturbance drift impact analysis, that is, to combine real-time environmental data to calculate the amount of environmental change when the potential field deviates from its ideal state, so that it can intelligently distinguish between "normal signal drift caused by environmental changes" and "abnormal signals caused by leakage". For example, it is known that there is clay (high resistivity) under area A. Real-time data shows that the soil moisture in area A increases by 20%. The dynamic compensation potential field will analyze that the increase in moisture → decrease in clay resistivity → decrease in potential at the corresponding position in area A. If the boundary is known to be an anchoring ditch, and heavy rainfall (external disturbance) is detected, it will analyze that the rainfall may cause water accumulation in the ditch → distortion of the potential field at the boundary. Finally, a precise spatial potential compensation strategy is output to eliminate false alarms or missed alarms caused by environmental interference.

[0031] Furthermore, step S300 also includes: using the structure identification channel in the dynamic compensation potential field model to determine the potential transmission path between adjacent spatial locations in the cross-interface potential detection network of the seepage barrier layer based on the spatial location characteristics of the detection unit; and using the regional medium distribution characteristics to calculate the equivalent conductivity parameters of each potential transmission path to form a reference potential correspondence; applying the soil water content data, temperature and humidity change data, regional conductivity change data, and external disturbance data in the environmental disturbance feature set to the equivalent conductivity parameter change at the corresponding spatial location, and calculating the influence of each environmental factor on the potential gradient change in the potential transmission path; activating the coupling analysis channel to analyze the combined effect of different environmental factors on the same potential transmission path based on the influence of each environmental factor on the potential transmission path, and determining the comprehensive change characteristics of the path potential transmission impedance; and using the comprehensive change characteristics to output a spatial potential compensation strategy.

[0032] Preferably, the structure identification channel is an analysis component in the dynamic compensation potential field model, used to analyze the input spatial topology information. Specifically, based on the spatial location characteristics of the detection units, the potential transmission path between adjacent spatial locations in the cross-interface potential detection network of the seepage barrier is determined. For example, the detection unit at coordinate (1,1) is connected to the detection units at coordinates (1,2) and (2,1). For each identified potential transmission path, the baseline conductivity is calculated based on the distribution characteristics of the medium in the area through which the path passes, such as dry sand, wet soil, or HDPE membrane, and the equivalent conductivity parameter is determined. The higher the equivalent conductivity parameter, the easier the path is to conduct electricity. Finally, a baseline potential correspondence is generated, clarifying the mathematical relationship that the potential between the detection units at both ends of each path should satisfy under ideal, interference-free conditions. For example, if detection units A and B are identified as neighbors and their path passes through a wet sand layer, the equivalent conductivity parameter G_AB = 0.5S (Siemens) of this path is calculated, indicating that the potential difference and current between detection units A and B should follow Ohm's law under the baseline condition.

[0033] Preferably, the soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data from the environmental disturbance feature set are applied to the equivalent conductivity parameter change at the corresponding spatial location. Specifically, increased moisture content leads to increased dielectric conductivity, which in turn leads to an increased equivalent conductivity parameter; increased temperature leads to increased ion activity, which in turn leads to increased conductivity, which in turn leads to an increased equivalent conductivity parameter; regional conductivity change data is a comprehensive or direct measurement of the previous two factors, directly causing a change in the equivalent conductivity parameter; external disturbance data, such as compaction, will compact the soil and change particle contact, thereby altering the equivalent conductivity parameter. The influence of each factor on each potential transmission path is quantified to determine the magnitude of the potential gradient change. For example, "current soil moisture..." A 10% increase in temperature will lead to a 0.02S increase in the equivalent conductivity parameter of path AB, resulting in a 0.01V / m decrease in the potential gradient along this path. Then, the coupling analysis channel is activated to analyze the combined effects of different environmental factors on the same potential transmission path based on the influence quantity. Temperature and humidity may enhance conductivity, producing a synergistic enhancement effect of "1+1>2"; or one factor may cancel out the influence of another. The coupling analysis channel quantifies the comprehensive change characteristics of the path potential transmission impedance by calculating the consistency coefficient and the difference coefficient, i.e., the total impedance characteristics of each potential transmission path after being changed by all environmental factors. Finally, a corresponding correction scheme is generated based on the comprehensive change characteristics. For example, for a certain path, if the conductivity is enhanced by 20% due to environmental factors, the measured potential difference needs to be reduced by X% to reflect the true leakage situation. The final output is a spatial potential compensation strategy accurate to the spatial location to counteract environmental interference and capture the true leakage signal.

[0034] Furthermore, step S300 also includes: performing time-series alignment processing on the influence quantities corresponding to different environmental disturbance factors to obtain the change response characteristics of the equivalent conductance parameters of each environmental factor to the same potential transmission path within the same time window; calculating the consistency coefficient and difference coefficient of the potential gradient change in the same potential transmission path based on the change response characteristics of each environmental factor; performing weighted coupling correction on the change of the equivalent conductance parameters of the potential transmission path according to the consistency coefficient and difference coefficient, and synchronously updating the potential transmission relationship between adjacent spatial locations; and forming a coupled spatial potential compensation strategy based on the updated potential transmission relationship.

[0035] Preferably, the data sampling frequencies of different sensors such as thermometers, hygrometers, moisture meters, and conductivity probes may differ. Time-series alignment processing is performed on the influence quantities corresponding to different environmental disturbances, that is, aligning all data at the same point in time. For each potential transmission path, the changes of various environmental factors within the same time window, such as the past 10 minutes, are observed, and whether they cause changes in the equivalent conductivity parameter (i.e., conductivity) of the path are determined, thereby determining the change response characteristics. Then, based on the change response characteristics of each environmental factor, the consistency coefficient and the difference coefficient of the potential gradient change in the same potential transmission path are calculated. The consistency coefficient measures whether the influence direction of two or more environmental factors on the same path is the same. High consistency is indicated when both temperature increase (conductivity ↑) and humidity increase (conductivity ↑) increase the conductivity parameter. The difference coefficient measures whether the influence intensity of each factor is different. High difference is indicated when humidity changes have a very large impact on conductivity, while temperature changes have a very small impact. Next, a weight is assigned to each influencing factor based on the consistency coefficient and the difference coefficient. The change in the equivalent conductivity parameter of the potential transmission path is then weighted and coupled for correction. If there is high consistency and high difference, the factors with greater influence are given higher weights. If there is low consistency, a "cancellation" operation is performed, which may even produce negative coupling terms. The potential transmission relationship between adjacent spatial locations is then updated synchronously based on the corrected total change. Finally, these are integrated into a spatial potential compensation strategy covering the entire seepage prevention area. This strategy can resist interference from complex and variable environments and solve the core problem of "false alarms / missed alarms caused by interference coupling" in complex engineering environments.

[0036] Step S400: After correcting the reference potential field of the cross-interface potential detection network of the seepage barrier layer using a spatial potential compensation strategy, a real-time reference potential field is established.

[0037] Step S400 further includes determining the equivalent conductivity parameters between each detection unit based on the spatial positional relationship of each detection unit in the cross-interface potential detection network of the impermeable layer, combined with the regional medium distribution characteristics; under calibration test environment conditions, calculating the steady-state potential distribution of the cross-interface potential detection network of the impermeable layer based on the equivalent conductivity parameters, and generating the reference potential value of each detection unit; and performing consistency calibration of the reference potential value according to the spatial positional relationship to form a reference potential field.

[0038] Preferably, before energizing the measurement, the spatial relationships and regional medium distribution characteristics of each detection unit in the cross-interface potential detection network of the geomembrane are input. Using a microscopic form such as Ohm's law or a resistance formula, combined with distance and medium resistivity, the equivalent conductivity parameters of the path are calculated, reflecting the ease with which current flows from one detection unit to another. Under calibration test conditions, the final state of the entire potential network is simulated using the calculated equivalent conductivity parameters of all paths and given excitation conditions. Specifically, the calibration test conditions are controllable, interference-free, and ideal environments. For example, the geomembrane is confirmed to be intact, there is no water accumulation or leakage above or below the geomembrane, environmental factors such as temperature and humidity are maintained at standard values, such as 20℃, 50%RH, and there are no external disturbances. Next, the entire impermeable layer is considered as a complex resistive network. Given the conductivity parameters of each potential transmission path, the known potential of some feedback units (0V), and the known excitation signal of some detection units, the theoretical potential value of each node in the network under equilibrium state is calculated by solving a large-scale Kirchhoff equation system, thus determining the reference potential value of each detection unit. Then, the consistency of the reference potential values ​​is calibrated according to the spatial relationship, including checking whether the calculated reference potential values ​​conform to physical laws and expectations. If the reference value of a certain unit deviates too much, it indicates that the medium distribution at that location does not match the input information, prompting a need for re-verification or compensation calibration. Finally, the reference potential values ​​of all detection units are combined to form a numerical matrix covering the entire impermeable area, determining the reference potential field representing the "health state" of the impermeable layer.

[0039] Preferably, a spatial potential compensation strategy is applied to the reference potential field for correction. For example, at the point with coordinates (10, 20), the reference potential needs to be reduced by 0.03V to reflect the real environment due to the high soil moisture. At the point with coordinates (50, 60), since it is a dry sand and gravel area, only 0.01V needs to be reduced. Then, the potential values ​​of all the detection units after correction are integrated to form a real-time reference potential field covering the entire seepage prevention area. This can accurately isolate environmental interference and amplify only the real abnormal signals caused by leakage, thus effectively combating complex environmental changes.

[0040] Step S500: After acquiring the detection data of the corresponding detection unit in the cross-interface potential detection network of the seepage barrier layer, establish a real-time response potential.

[0041] Preferably, the detection data of the detection unit is its real-time potential difference relative to the common reference ground (the feedback unit below the seepage barrier). Specifically, the voltage signal of each detection unit is read sequentially or in parallel according to the set time sequence by the outdoor acquisition host. The original voltage signal may be very weak and mixed with noise. The circuit in the acquisition host amplifies and filters it to improve the signal-to-noise ratio. The processed analog voltage signal is converted into a digital signal by the analog-to-digital converter. The original acquisition data of all detection units are then stored in a structured manner according to their spatial location. Each detection data is marked with a timestamp and spatial coordinate label. Finally, the function or matrix related to the spatial location is determined, namely the real-time response potential, which represents the true potential value measured by the detection unit at coordinate (x, y).

[0042] Step S600: Perform differential offset analysis on the real-time response potential and the real-time reference potential field, and perform cross-node correlation analysis through the spatial topology relationship of the cross-interface potential detection network of the seepage barrier layer to establish an anomaly early warning.

[0043] Step S600 further includes performing point-by-point differential calculations on the real-time response potential and the real-time reference potential field based on the spatial position correspondence to establish the potential offset of each detection unit; performing correlation analysis of the potential offset in the spatial topological relationship to identify diffusion trend characteristics, and using the diffusion trend characteristics to establish anomaly warning.

[0044] Preferably, differential offset analysis is performed on the input real-time response potential and real-time reference potential field. This involves point-by-point differential calculation for each detection unit at each spatial location to determine a potential offset. If the potential offset is approximately 0, the measured value is consistent with the dynamic standard, indicating that the potential change at that point can be fully explained by environmental factors, with no signs of leakage. If the potential offset is significantly greater than 0, it indicates that the measured value deviates from the dynamic standard, suggesting the existence of other disturbance sources besides environmental factors. In a seepage prevention scenario, this disturbance source is highly likely to be a conductive channel caused by leakage. The potential offset of each detection unit is then output, visually displaying the anomalies at each point in the entire seepage prevention area. Neighborhood aggregation analysis is used to analyze the correlation between abnormal potential offset points in the spatial topology, including checking the abnormal potential offset points. The abnormal potential shifts can occur in isolated or clustered locations, be randomly distributed or exhibit a pattern, and show varying intensities, with the center being higher and the surrounding areas lower, or one side higher and the other lower. Multiple analyses are performed continuously, such as comparing the potential shifts of each detection unit between the current and previous moments to calculate the changes in the intensity and direction of the shift, thus determining the diffusion trend characteristics. Finally, based on the identified "static abnormal intensity" and "dynamic diffusion trend," different levels of early warning information are intelligently generated. For example, a level one warning might indicate a minor, stable leak or localized disturbance; a level two warning confirms a leak with increasing risk; and a level three warning indicates a rapid diffusion trend, suggesting that a severe leak may lead to structural failure and requires immediate action. Furthermore, the warning content includes the leak source identifier, radiation intensity path, and action recommendations.

[0045] Furthermore, step S600 also includes extracting the spatial adjacency relationship of each detection unit in the cross-interface potential detection network of the seepage barrier as a spatial topology relationship; using the spatial topology relationship to perform neighborhood aggregation processing on the potential offset of each detection unit to obtain the offset aggregation intensity of each detection unit in the local spatial range; and identifying the continuous propagation direction and diffusion path of the potential offset in the spatial topology relationship based on the difference in the offset aggregation intensity between adjacent detection units to generate diffusion trend features.

[0046] Preferably, the spatial adjacency relationship of each detection unit in the cross-interface potential detection network of the seepage barrier is obtained and used as a spatial topological relationship. Then, the potential offset of each detection unit is processed by neighborhood aggregation, i.e., spatial filtering or convolution operation is performed, and weighted summation is used to obtain the offset aggregation intensity of each detection unit in the local spatial range. This represents the abnormal heat of the local area where each detection unit is located, which highlights the center of the abnormal area and suppresses isolated noise points. In a continuous physical field, the intensity of the abnormal leakage point is the highest, and the intensity decreases with distance from the abnormal leakage point. The "direction" is from "high aggregation intensity" to "adjacent low aggregation intensity". By comparing the aggregation intensity between adjacent detection units, the path of abnormal diffusion is identified. Specifically, local peaks are found to determine the potential abnormal source center. Starting from each peak point, the path is traced along the direction of the fastest intensity decrease. The coordinates of the detection units passed through are recorded in sequence to form a diffusion path. The direction is from the first peak point to the last point on the path, thereby accurately identifying the source, propagation direction and diffusion path of the abnormal leakage, and outputting the diffusion trend characteristics.

[0047] Furthermore, step S600 also includes: after performing source tracing and location based on the potential offset, establishing a leakage source identifier; using the diffusion trend characteristics to perform radiation correlation analysis of the leakage source and establish a radiation intensity path; configuring an abnormal early warning signal according to the leakage source identifier and radiation intensity path, and executing an early warning.

[0048] Preferably, after obtaining the potential offset of all detection units, the most likely specific location of the leakage is deduced using the peak positioning method, i.e., the location of the detection unit with the largest offset is found. Since the potential difference is greater the closer to the leakage point, it is the most direct identifier of the leakage source. Centered on the leakage source, and combined with the identified diffusion trend characteristics, the range and intensity distribution of the leakage impact are analyzed. This includes checking the attenuation of the offset aggregation intensity of each detection unit along the diffusion direction, and establishing a radiation intensity path. That is, along the propagation direction defined by the diffusion trend characteristics, starting from the leakage source, extending outward along the diffusion direction, each node has its coordinates and estimated impact intensity. Finally, based on the leakage source identifier and radiation intensity path, abnormal early warning signals are configured, and early warnings are issued through sound and light, software pop-ups, SMS, etc. For example, a blue Level 1 warning may indicate a minor leakage or local interference; a yellow Level 2 warning indicates a significant diffusion trend, confirming leakage and increasing risk; an orange Level 3 warning indicates severe leakage that may endanger structural safety; and a red Level 4 warning indicates the diffusion path has reached the boundary of the seepage prevention layer or a critical structure, requiring immediate emergency measures. Based on the warning level and location, it is recommended to check the corresponding area's sensors or records, dispatch personnel to investigate near the source point coordinates, activate the local repair plan to strengthen monitoring along the radiation path, and immediately activate the emergency response to evacuate downstream areas. This will ensure improved real-time response to leaks, accurate leak point location, dynamic compensation capabilities for environmental disturbances, and long-term stability of engineering monitoring.

[0049] In the above text, refer to Figure 1 A method for monitoring engineering seepage prevention based on potential difference changes according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 A potential difference-based engineering seepage prevention monitoring system according to an embodiment of the present invention is described.

[0050] The engineering seepage prevention monitoring system based on potential difference change according to embodiments of the present invention addresses the technical problems of existing technologies, such as slow response, low positioning accuracy, poor environmental adaptability, limited monitoring coverage, and difficulty in achieving real-time online monitoring. It achieves the technical effects of improving the real-time performance of seepage response, the accuracy of seepage point location, the dynamic compensation capability for environmental disturbances, and the long-term stability of engineering monitoring. Figure 2 As shown, the engineering seepage prevention monitoring system based on potential difference changes includes: a detection network construction module 10, an environmental response data acquisition module 20, a compensation strategy output module 30, a potential field correction module 40, a real-time response potential establishment module 50, and an anomaly early warning analysis module 60.

[0051] The detection network construction module 10 is used to deploy detection units in the seepage-proof layer of the target project's seepage-proof area, and construct a cross-interface potential detection network for the seepage-proof layer using the spatial relationship of the detection units; the environmental response data acquisition module 20 is used to continuously acquire multi-source environmental response data during the operation of the seepage-proof area of ​​the target project, including soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data, and construct an environmental disturbance feature set using the multi-source environmental response data; the compensation strategy output module 30 is used to input the environmental disturbance feature set into the dynamic... The system includes a compensation potential field model, which outputs a spatial potential compensation strategy; a potential field correction module 40, which uses the spatial potential compensation strategy to correct the reference potential field of the cross-interface potential detection network of the seepage barrier layer and establish a real-time reference potential field; a real-time response potential establishment module 50, which establishes a real-time response potential after acquiring the detection data of the corresponding detection unit in the cross-interface potential detection network of the seepage barrier layer; and an anomaly early warning analysis module 60, which performs differential offset analysis on the real-time response potential and the real-time reference potential field, and performs cross-node correlation analysis through the spatial topology relationship of the cross-interface potential detection network of the seepage barrier layer to establish an anomaly early warning.

[0052] The specific configuration of the compensation strategy output module 30 will be described in detail below. The compensation strategy output module 30 further includes: extracting spatial topological information of the seepage-proof area of ​​the target project using a cross-interface potential detection network of the seepage-proof layer; the spatial topological information includes spatial distribution characteristics of the seepage-proof layer, spatial location characteristics of the detection units, regional medium distribution characteristics, and structural boundary constraint relationships; pre-inputting the spatial distribution characteristics of the seepage-proof layer into the dynamic compensation potential field model to perform model initialization processing; inputting the set of environmental disturbance characteristics into the initialized dynamic compensation potential field model, performing environmental disturbance drift influence analysis, and outputting a spatial potential compensation strategy.

[0053] The specific configuration of the compensation strategy output module 30 will be described in detail below. The compensation strategy output module 30 further includes: using the structure identification channel in the dynamic compensation potential field model to determine the potential transmission path between adjacent spatial locations in the cross-interface potential detection network of the seepage barrier layer based on the spatial location characteristics of the detection unit; calculating the equivalent conductivity parameters of each potential transmission path using the regional medium distribution characteristics to form a reference potential correspondence; applying the soil moisture state data, temperature and humidity change data, regional conductivity change data, and external disturbance data from the environmental disturbance feature set to the equivalent conductivity parameter change at the corresponding spatial location, calculating the influence of each environmental factor on the potential gradient change in the potential transmission path; activating the coupling analysis channel to analyze the combined effect of different environmental factors on the same potential transmission path based on the influence of each environmental factor on the potential transmission path, determining the comprehensive change characteristics of the path potential transmission impedance; and outputting the spatial potential compensation strategy using the comprehensive change characteristics.

[0054] The specific configuration of the compensation strategy output module 30 will be described in detail below. The compensation strategy output module 30 further includes: performing time-series alignment processing on the influence quantities corresponding to different environmental disturbance factors to obtain the change response characteristics of the equivalent conductance parameters of each environmental factor to the same potential transmission path within the same time window; calculating the consistency coefficient and difference coefficient of the potential gradient change in the same potential transmission path based on the change response characteristics of each environmental factor; performing weighted coupling correction on the change in the equivalent conductance parameters of the potential transmission path based on the consistency coefficient and difference coefficient, and synchronously updating the potential transmission relationship between adjacent spatial locations; and forming a coupled spatial potential compensation strategy based on the updated potential transmission relationship.

[0055] The specific configuration of the potential field correction module 40 will be described in detail below. The potential field correction module 40 further includes: determining the equivalent conductivity parameters between each detection unit based on the spatial positional relationship of each detection unit in the cross-interface potential detection network of the impermeable layer, combined with the regional medium distribution characteristics; calculating the steady-state potential distribution of the cross-interface potential detection network of the impermeable layer based on the equivalent conductivity parameters under calibration test conditions, generating a reference potential value for each detection unit; and performing consistency calibration of the reference potential values ​​according to the spatial positional relationship to form a reference potential field.

[0056] The specific configuration of the anomaly warning analysis module 60 will be described in detail below. The anomaly warning analysis module 60 further includes: performing point-by-point differential calculations on the real-time response potential and the real-time reference potential field based on the spatial position correspondence to establish the potential offset of each detection unit; performing correlation analysis of the potential offset in the spatial topological relationship to identify diffusion trend characteristics, and using the diffusion trend characteristics to establish anomaly warning.

[0057] The specific configuration of the anomaly early warning analysis module 60 will be described in detail below. The anomaly early warning analysis module 60 further includes: extracting the spatial adjacency relationships of each detection unit in the cross-interface potential detection network of the seepage barrier as a spatial topological relationship; using the spatial topological relationship to perform neighborhood aggregation processing on the potential offset of each detection unit to obtain the offset aggregation intensity of each detection unit within a local spatial range; identifying the continuous propagation direction and diffusion path of the potential offset in the spatial topological relationship based on the difference in the offset aggregation intensity between adjacent detection units, and generating diffusion trend characteristics.

[0058] The specific configuration of the anomaly warning analysis module 60 will be described in detail below. The anomaly warning analysis module 60 further includes: establishing a leakage source identifier after performing source tracing and positioning based on the potential offset; performing radiation correlation analysis of the leakage source using the diffusion trend characteristics to establish a radiation intensity path; configuring anomaly warning signals according to the leakage source identifier and radiation intensity path, and issuing an early warning.

[0059] The specific configuration of the detection network construction module 10 will be described in detail below. The detection network construction module 10 further includes: detection units encapsulated in graphene and protected with HDPE material, which are fixed to the structural layer of the seepage prevention area of ​​the target project by heat fusion.

[0060] The engineering seepage prevention monitoring system based on potential difference change provided in the embodiments of the present invention can execute the engineering seepage prevention monitoring method based on potential difference change provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for monitoring engineering seepage prevention based on changes in potential difference, characterized in that, The method includes: Detection units are deployed in the seepage prevention layer of the target project's seepage prevention area, and a cross-interface potential detection network for the seepage prevention layer is constructed using the spatial relationship of the detection units. Continuously acquire multi-source environmental response data during the operation of the seepage prevention area of ​​the target project. The multi-source environmental response data includes soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data. Construct an environmental disturbance feature set using the multi-source environmental response data. The set of environmental disturbance features is input into the dynamic compensation potential field model, and the spatial potential compensation strategy is output. After correcting the reference potential field of the cross-interface potential detection network of the impermeable layer using a space potential compensation strategy, a real-time reference potential field is established. After acquiring the detection data of the corresponding detection unit in the cross-interface potential detection network of the seepage barrier, a real-time response potential is established; Differential offset analysis is performed on the real-time response potential and real-time reference potential field, and cross-node correlation analysis is performed through the spatial topology relationship of the cross-interface potential detection network of the seepage barrier layer to establish an anomaly early warning.

2. The engineering seepage prevention monitoring method based on potential difference change as described in claim 1, characterized in that, The environmental disturbance feature set is input into the dynamic compensation potential field model, and the spatial potential compensation strategy is output, including: Spatial topology information of the seepage prevention area of ​​the target project is extracted by using a cross-interface potential detection network of the seepage prevention layer. The spatial topology information includes the spatial distribution characteristics of the seepage prevention layer, the spatial location characteristics of the detection unit, the regional medium distribution characteristics, and the structural boundary constraint relationship. The spatial distribution characteristics of the impermeable layer are pre-input into the dynamic compensation potential field model to perform model initialization processing; The environmental disturbance feature set is input into the initialized dynamic compensation potential field model, and the environmental disturbance drift influence analysis is performed to output the spatial potential compensation strategy.

3. The engineering seepage prevention monitoring method based on potential difference change as described in claim 2, characterized in that, The set of environmental disturbance features is input into the initialized dynamic compensation potential field model, including: The potential transmission path between adjacent spatial locations in the cross-interface potential detection network of the seepage barrier is determined based on the spatial location characteristics of the detection unit using the structural identification channel in the dynamic compensation potential field model. The equivalent conductivity parameters of each potential transmission path are calculated using the regional medium distribution characteristics to form a reference potential correspondence. The soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data in the environmental disturbance feature set are applied to the equivalent conductivity parameter change at the corresponding spatial location, and the influence of each environmental factor on the potential gradient change in the potential transmission path is calculated. The activation of the coupling analysis channel analyzes the combined effects of different environmental factors on the same potential transmission path based on the influence of various environmental factors on the potential transmission path, and determines the comprehensive variation characteristics of the path potential transmission impedance. The spatial potential compensation strategy is output using the comprehensive variation characteristics.

4. The engineering seepage prevention monitoring method based on potential difference change as described in claim 3, characterized in that, The activated coupling analysis channel analyzes the combined effects of different environmental factors on the same potential transport path based on the influence of various environmental factors on the potential transport path, including: The influence quantities corresponding to different environmental disturbance factors are time-series aligned to obtain the change response characteristics of the equivalent conductance parameters of the same potential transmission path for each environmental factor within the same time window. Calculate the consistency coefficient and difference coefficient of potential gradient change in the same potential transport path based on the response characteristics of various environmental factors. The equivalent conductivity parameter change of the potential transmission path is weighted and coupled to correct the change based on the consistency coefficient and the difference coefficient, and the potential transmission relationship between adjacent spatial locations is updated synchronously. A coupled space potential compensation strategy is formed based on the updated potential transport relationship.

5. The engineering seepage prevention monitoring method based on potential difference change as described in claim 1, characterized in that, The construction of the reference electric potential field includes: Based on the spatial positional relationship of each detection unit in the cross-interface potential detection network of the seepage barrier layer, and combined with the regional medium distribution characteristics, the corresponding equivalent conductivity parameters between each detection unit are determined. Under calibrated test conditions, the steady-state potential distribution of the cross-interface potential detection network of the seepage barrier layer is calculated based on the equivalent conductivity parameters to generate the reference potential value of each detection unit. The reference potential values ​​are calibrated for consistency according to their spatial location to form a reference potential field.

6. The engineering seepage prevention monitoring method based on potential difference change as described in claim 1, characterized in that, Establish anomaly warning systems, including: The real-time response potential and real-time reference potential field are subjected to point-by-point differential calculation based on the spatial position correspondence to establish the potential offset of each detection unit; Perform correlation analysis of potential offset in spatial topology to identify diffusion trend characteristics, and use these characteristics to establish anomaly early warning.

7. The engineering seepage prevention monitoring method based on potential difference change as described in claim 6, characterized in that, Identify diffusion trend characteristics, including: The spatial adjacency relationship of each detection unit in the cross-interface potential detection network of the seepage barrier layer is extracted as the spatial topology relationship; The potential offset of each detection unit is aggregated in a neighborhood using the spatial topology to obtain the offset aggregation intensity of each detection unit in a local spatial range. Based on the difference in the intensity of the offset aggregation between adjacent detection units, the continuous propagation direction and diffusion path of the potential offset in the spatial topology are identified, and diffusion trend features are generated.

8. The engineering seepage prevention monitoring method based on potential difference change as described in claim 6, characterized in that, Establishing anomaly early warning systems based on diffusion trend characteristics also includes: After performing source tracing and location based on the aforementioned potential offset, a leak source identifier is established; Radiation correlation analysis of the leakage source is performed using the aforementioned diffusion trend characteristics to establish radiation intensity paths; Based on the leakage source identification and radiation intensity path configuration, an abnormal early warning signal is issued and an early warning is executed.

9. The engineering seepage prevention monitoring method based on potential difference change as described in claim 1, characterized in that, The detection unit is encapsulated in graphene and protected with HDPE material, and is fixed to the structural layer of the seepage prevention area of ​​the target project by hot-melt method.

10. An engineering seepage prevention monitoring system based on potential difference changes, characterized in that, The system is used to implement the engineering seepage prevention monitoring method based on potential difference change as described in any one of claims 1 to 9, and the system comprises: The detection network construction module is used to deploy detection units in the seepage prevention layer of the target project's seepage prevention area, and to construct a cross-interface potential detection network for the seepage prevention layer using the spatial relationship of the detection units. The environmental response data acquisition module is used to continuously acquire multi-source environmental response data during the operation of the seepage prevention area of ​​the target project. The multi-source environmental response data includes soil moisture content data, temperature and humidity change data, regional conductivity change data, and external disturbance data. The multi-source environmental response data is used to construct an environmental disturbance feature set. The compensation strategy output module is used to input the set of environmental disturbance features into the dynamic compensation potential field model and output the spatial potential compensation strategy. The potential field correction module is used to correct the reference potential field of the cross-interface potential detection network of the seepage barrier layer using a spatial potential compensation strategy, and then establish a real-time reference potential field. The real-time response potential establishment module is used to establish a real-time response potential after acquiring the detection data of the corresponding detection unit in the cross-interface potential detection network of the seepage barrier layer; The anomaly warning and analysis module is used to perform differential offset analysis on real-time response potential and real-time reference potential field, and to perform cross-node correlation analysis through the spatial topology relationship of the cross-interface potential detection network of the seepage barrier layer to establish anomaly warning.

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